Techniques for processing perceived routability constraints that may or may not affect movement of a mobile device within an indoor environment

ABSTRACT

Techniques are provided which may be implemented using various methods, apparatuses and/or articles of manufacture to provide or otherwise support mobile device positioning. The mobile device positioning may be based, at least in part, on one or more perceived routability constraints that may or may not affect actual movement of a mobile device within an indoor environment.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

This application claims priority under 35 USC 119 to U.S. ProvisionalApplication Ser. No. 61/622,334, filed Apr. 10, 2012, and entitled,“TECHNIQUES FOR PROCESSING PERCEIVED ROUTABILITY CONSTRAINTS THAT MAY ORMAY NOT AFFECT MOVEMENT OF A MOBILE DEVICE WITHIN AN INDOORENVIRONMENT”, which is assigned to the assignee here of, and which isincorporated herein by reference.

BACKGROUND

1. Field

The subject matter disclosed herein relates to electronic devices, andmore particularly to methods, apparatuses and articles of manufacturefor use in one or more computing platforms to provide or otherwisesupport mobile device positioning based, at least in part, on one ormore perceived routability constraints that may or may not affectmovement of a mobile device within an indoor environment.

2. Information

Mobile devices, such as mobile phones, notebook, computers, etc.,typically have the ability to estimate location and/or position with ahigh degree of precision using any one of several technologies such as,for example, satellite positioning systems (e.g., GPS and the like),advanced forward-link trilateration (AFLT), just to name a few examplesof signal-based positioning systems and/or corresponding signal signals.Using high precision location information, applications for a mobiledevice may provide a user with many different services such as, forexample, vehicle/pedestrian navigation, location-based searching, justto name a couple of examples. Here, high precision signal-based locationinformation (e.g., received from GPS and/or other signal-basedpositioning systems) may be processed according to a global coordinatesystem (e.g., latitude and longitude or earth-centered xyz coordinates).While such use of signal-based location information referenced to aglobal coordinate system may be useful in providing some services (e.g.,outdoor vehicle navigation), such signal-based location informationreferenced to a global coordinate system may be impractical for othertypes of services such as indoor pedestrian navigation.

in certain indoor environments, such as office buildings, shoppingmalls, airports, stadiums, etc., certain example signal-basedpositioning techniques may make use of various terrestrial-basedwireless signal transmitting devices, e.g., wireless network accesspoints, cellular network base stations, special-purpose beacontransmitters, etc., that transmit wireless signals which may be receivedby the mobile device and used for positioning purposes. For example, amobile device may receive a signal-based positioning signal from atransmitter and based thereon determine a range between the transmitterand receiver. Hence, for example, positioning may be provided based ontrilateration and/or other known signal-based positioning techniques.

SUMMARY

In accordance with certain aspects, a method may be provided forsupporting mobile device positioning. The method may comprise, with atleast one computing platform: receiving an electronic map for an indoorenvironment, wherein at least a portion of the electronic map comprisesdisplayable image data; identifying at least one perceived routabilityconstraint based, at least in part, on the displayable image data;setting a measure of confidence for the at least one perceivedroutability constraint based, at least in part, on the displayable imagedata; and if the computing platform is part of a mobile device,estimating a trajectory of the mobile device within the indoorenvironment based, at least in part, on the measure of confidence,otherwise if the computing platform as part of a computing deviceexternal to the mobile device, transmitting at least the measure ofconfidence to the mobile device, e.g., for use in estimating thetrajectory of the mobile device within the indoor environment.

In accordance with yet certain other aspects, an apparatus may beprovided for use in a mobile device, wherein the apparatus comprises:means for receiving an electronic map for an indoor environment, whereinat least a portion of the electronic map comprises displayable imagedata; means for identifying at least one perceived routabilityconstraint based, at least in part, on the displayable image data; meansfor setting a measure of confidence for the at least one perceivedroutability constraint based, at least in part, on the displayable imagedata; means for estimating a trajectory of the mobile device within theindoor environment based, at least in part, on the measure ofconfidence; and means for presenting estimated positioning informationto the user of the mobile device, the positioning information beingbased, at least in part, on the trajectory of the mobile device withinthe indoor environment.

In accordance with certain further aspects, an apparatus may be providedfor use in a computing device is external to the mobile device tosupport mobile device positioning. Here, for example, such an apparatusmay comprise: means for receiving an electronic map for an indoorenvironment, wherein at least a portion of the electronic map comprisesdisplayable image data; means for identifying at least one perceivedroutability constraint based, at least in part, on the displayable imagedata; means for setting a measure of confidence for the at least oneperceived routability constraint based, at least in part, on thedisplayable image data; and means for transmitting at least the measureof confidence to the mobile device for use in estimating the trajectoryof the mobile device within the indoor environment.

In accordance with still certain other aspects, a mobile device may beprovided which comprises: memory; an output unit; and one or moreprocessing units to: access an electronic map for an indoor environmentvia the memory, wherein at least a portion of the electronic mapcomprises displayable image data; identify at least one perceivedroutability constraint based, at least in part, on the displayable imagedata; set a measure of confidence for the at least one perceivedroutability constraint based, at least in part, on the displayable imagedata; estimate a trajectory of the mobile device within the indoorenvironment based, at least in part, on the measure of confidence; andinitiate presentation of estimated positioning information to the userof the mobile device via the output unit, the positioning informationbeing based, at least in part, on the trajectory of the mobile devicewithin the indoor environment.

In accordance with still other aspects, a computing device may beprovided for use in supporting mobile device positioning. Here, forexample, such a computing device may comprise: memory; a communicationinterface; and one or more processing units to: access an electronic mapfor an indoor environment via the memory, wherein at least a portion ofthe electronic map comprises displayable image data; identify at leastone perceived routability constraint based, at least in part, on thedisplayable image data; set a measure of confidence for the at least oneperceived routability constraint based, at least in part, on thedisplayable image data; and initiate transmission, via the communicationinterface, of at least the measure of confidence to the mobile devicefor use in estimating the trajectory of the mobile device within theindoor environment.

In accordance with still other aspects, an article of manufacture may beprovided for use in a computing device to support mobile devicepositioning. Here, for example, such an article of manufacture maycomprise a non-transitory computer readable medium having computerimplementable instructions stored therein that are executable by one ormore processing units of a computing platform of a mobile device to:receive an electronic map for an indoor environment, wherein at least aportion of the electronic map comprises displayable image data; identifyat least one perceived routability constraint based, at least in part,on the displayable image data; set a measure of confidence for the atleast one perceived routability constraint based, at least in part, onthe displayable image data; estimate a trajectory of the mobile devicewithin the indoor environment based, at least in part, on the measure ofconfidence; and initiate presentation of estimated positioninginformation to the user of the mobile device, the positioninginformation being based, at least in part, on the trajectory of themobile device within the indoor environment.

In accordance with still further aspects, an article of manufacture maybe provided for use in a computing platform that is external to a mobiledevice to support mobile device positioning. Here, for example such anarticle of manufacture may comprise a non-transitory computer readablemedium having computer implementable instructions stored therein thatare executable by one or more processing units of the computing platformto: receive an electronic map for an indoor environment, wherein atleast a portion of the electronic map comprises displayable image data;identify at least one perceived routability constraint based, at leastin part, on the displayable image data; set a measure of confidence forthe at least one perceived routability constraint based, at least inpart, on the displayable image data; and initiate transmission of atleast the measure of confidence to the mobile device for use inestimating the trajectory of the mobile device within the indoorenvironment.

According to another aspect, a method may be provided for a mobiledevice, wherein the method comprises: receiving a measure of confidencefor at least one perceived routability constraint identified based, atleast in part, on displayable image data of an electronic map for anindoor environment; estimating a trajectory of the mobile device withinthe indoor environment based, at least in part, on the measure ofconfidence for the at least one perceived routability constraint; andpresenting estimated positioning information to the user of the mobiledevice, the positioning information being based, at least in part, onthe trajectory of the mobile device within the indoor environment.

According to a further aspect, an apparatus to provide mobile devicepositioning may comprise: means for receiving a measure of confidencefor at least one perceived routability constraint identified based, atleast in part, on displayable image data of an electronic map for anindoor environment; means for estimating a trajectory of the mobiledevice within the indoor environment based, at least in part, on themeasure of confidence for the at least one perceived routabilityconstraint; and means for presenting estimated positioning informationto the user of the mobile device, the positioning information beingbased, at least in part, on the trajectory of the mobile device withinthe indoor environment.

According to certain further aspects, a mobile device may be providedhaving a positioning capability, wherein the mobile device comprises:memory; an output unit; and one or more processing units to: receive,via the memory, a measure of confidence for at least one perceivedroutability constraint identified based, at least in part, ondisplayable image data of an electronic map for an indoor environment;estimate a trajectory of the mobile device within the indoor environmentbased, at least in part, on the measure of confidence for the at leastone perceived routability constraint; and initiate presentation ofestimated positioning information to the user of the mobile device viathe output unit, the positioning information being based, at least inpart, on the trajectory of the mobile device within the indoorenvironment.

According to yet another aspect, in article of manufacture may beprovided for use in a mobile device to provide for mobile devicepositioning. Here, for example, such an article of manufacture maycomprise a non-transitory computer readable medium having computerimplementable instructions stored therein that are executable by one ormore processing units of a computing platform to: receive a measure ofconfidence for at least one perceived routability constraint identifiedbased, at least in part, on displayable image data of an electronic mapfor an indoor environment; estimate a trajectory of the mobile devicewithin the indoor environment based, at least in part, on the measure ofconfidence for the at least one perceived routability constraint; andinitiate presentation of estimated positioning information to the userof the mobile device, the positioning information being based, at leastin part, on the trajectory of the mobile device within the indoorenvironment.

BRIEF DESCRIPTION OF DRAWINGS

Non-limiting and non-exhaustive aspects are described with reference tothe following figures, wherein like reference numerals refer to likeparts throughout the various figures unless otherwise specified.

FIG. 1 is a schematic block diagram illustrating an example environmentthat includes a computing device and a mobile device, one or more ofwhich may provide or otherwise support mobile device positioning based,at least in part, on one or more perceived routability constraints thatmay or may not affect actual movement of a mobile device within anindoor environment, in accordance with an example implementation.

FIG. 2 is a schematic block diagram illustrating certain features of anexample computing platform in a computing device to provide or otherwisesupport mobile device positioning based, at least in part, on one ormore perceived routability constraints, in accordance with an exampleimplementation.

FIG. 3 is a schematic block diagram illustrating certain features of anexample computing platform in a mobile device to provide or otherwisesupport mobile device positioning based, at least in part, on one ormore perceived routability constraints, in accordance with an exampleimplementation.

FIG. 4 is a diagram showing certain example physical features of anindoor environment that may be represented in an electronic map andwhich may act as obstacles to movement of a person within the indoorenvironment, in accordance with an example implementation.

FIG. 5 is a diagram showing an indoor environment as shown in FIG. 4along with corresponding displayable image data overlaid on certainphysical features, in accordance with an example implementation.

FIG. 6 is a diagram showing an example depiction of displayable imagedata as in FIG. 5 that has been further intentionally annotated withadditional displayable image data that may not represent physicalfeatures, in accordance with an example implementation.

FIG. 7 is a diagram showing an example depiction of displayable imagedata as in FIG. 6 further illustrated with unintended additionaldisplayable image data and/or distorted displayable image data, inaccordance with an example implementation.

FIG. 8 is a diagram showing an example pixel array that may be used todisplay displayable image data, in accordance with an exampleimplementation.

FIG. 9 is a diagram showing an example depiction of displayable imagedata as in FIG. 6 and a corresponding routability graph, in accordancewith an example implementation.

FIG. 10 is a diagram showing an example depiction of displayable imagedata and routability graph as in FIG. 9, and further illustrating asimulated trajectory and a possible estimated trajectory of a mobiledevice with respect to the routability graph in the example indoorenvironment, in accordance with an example implementation.

FIG. 11 is a flow diagram illustrating an example process that may beimplemented in whole or in part in a computing platform of a computingdevice and/or a mobile device to provide or otherwise support mobiledevice positioning based, at least in part, on one or more perceivedroutability constraints that may or may not affect movement of a mobiledevice within an indoor environment, in accordance with an exampleimplementation.

FIG. 12 is a flow diagram illustrating an example process that may beimplemented in whole or in part in a computing platform of a mobiledevice to provide or otherwise support mobile device positioning based,at least in part, on one or more perceived routability constraints thatmay or may not affect movement of a mobile device within an indoorenvironment, in accordance with an example implementation.

DETAILED DESCRIPTION

As illustrated by the examples herein, various methods, apparatuses andarticles of manufacture may be implemented for use in one or morecomputing platforms to provide or otherwise support mobile devicepositioning based, at least in part, on one or more perceivedroutability constraints that may or may not affect actual movement of amobile device (e.g., while being carried by person) within an indoorenvironment.

In accordance with certain example implementations, one or morecomputing platforms in one or more electronic devices may be provided toreceive an electronic map for an indoor environment, wherein at least aportion of the electronic map comprises displayable image data. Anelectronic map may comprise various forms or formats of data fileshaving displayable image data. An electronic map may, for example,comprise displayable image data associated with one or more drawing fileformats, image file formats, and/or the like or some combinationthereof.

For example, in certain implementations, displayable image data maycomprise vector-based image data, raster-based image data, and/or thelike or some combination thereof. As such, in certain exampleimplementations displayable image data may comprise a Computer-AidedDesign (CAD), Computer Graphics Metafile (CGM), Scalable Vector Graphics(SVG), and/or other like drawings which may use, at least in part,vector-based image data. For example in certain implementationsdisplayable image data may comprise one or more file formats, such as,e.g., Joint Photographic Experts Group (JPEG), Exchangeable Image FileFormat (Exif), Tagged Image File Format (TIFF), Portable NetworkGraphics (PNG), Graphics Interchange Format (GIF), and/or other likeimage files which may use, at least in part, raster-based image data.For example in certain implementations displayable image data maycomprise one or more file formats which may use, at least in part, oneor more of vector-based image data and/or raster-based image data, suchas, e.g., PostScript, Encapsulated PostScript (EPS), Portable DocumentFormat (PDF), a QuickDraw file (PICT), and/or other like forms of imagefiles. In certain further implementations, displayable image data maycomprise three-dimensional imaging and/or other like stereo formattedfiles.

As described in greater detail herein, there may be some displayableimage data within an electronic map that may have been intentionallyadded and/or unintentionally added and/or altered in some manner at somepoint in processing (e.g., generating, copying, encoding, storing, etc.)the electronic map. Consequently, there may be one or more perceivedroutability constraints within the displayable image data that may ormay not affect actual movement of the mobile device within the indoorenvironment. A perceived routability constraint, in a general sense, maycomprise an actual physical object that may be present in the actualindoor environment and which is intentionally represented in some mannerby the data encoded within a corresponding electronic map. Additionally,in certain instances, such an electronic map may also include other datathat may be mistaken as representing an actual physical object withinthe indoor environment, when in fact no such actual object actuallyexists within the indoor environment. It may be useful to be able todetermine whether or not such data encoded within an electronic maprepresents one or more actual physical objects that may affectnavigation within the indoor environment. By way of an initial example,in certain instances, various annotating text (e.g., room designations,descriptors, measurements, notes, etc.) and/or graphics (e.g., icons,symbols, color coding, etc.) may be added to displayable image data.Since, such annotating text and/or graphics would likely not be presentin the actual indoor environment, it may be beneficial to identifyand/or otherwise handle such perceived routability constraints in amanner that provides some level of flexibility with regard to estimatinga trajectory of the mobile device within the indoor environment.

Similarly, it may be beneficial to identify and/or otherwise handleperceived routability constraints that may relate to displayable imagedata that was not intended to represent actual physical obstacles and/orwas not intended to be part of an electronic map in the first place. Forexample, various perceived routability constraints may actually relateto simple processing induced alterations, annotations, noise, errors,and/or the like or some combination thereof. For example, displayableimage data may be corrupted and/or otherwise altered in some manner incopying or transferring it from one format to another, or possiblychanging its size or scale or resolution, or possibly affecting itscolor or contrast, or in expanding/reducing the size of one or morecorresponding data file(s) size(s). For example, vector-based image dataprovided in an electronic map may or may not properly represent thescale of certain obstacles within an indoor environment; e.g., a line incertain vector-based image data may represent to scale a length of awall or other like object that it represents, but not necessarilyrepresent to scale the width of such a wall or other like object. Forexample, raster-based image data provided in electronic map may or maynot properly represent a scale of certain obstacles within an indoorenvironment; e.g., an object transition in certain raster-based imagedata may be dithered and/or otherwise encoded in some manner that apoint of actual demarcation between adjacent objects and/or other likedistinguishable regions may not be distinguished with enough specificityto be considered accurate under certain conditions. Here, for example,certain raster-based image data may at certain resolutions presentsomewhat fuzzy lines or regions due to the encoding of pixels (e.g.,pixel values, etc.) for use with an array of pixels in a display device,or possibly in a printing device.

In accordance with certain example implementations, at least oneperceived routability constraint may be identified and a measure ofconfidence for such perceived routability constraint may be set based,at least in part, on the displayable image data. Such a measure ofconfidence may, for example, be made available for use in estimating atrajectory of a mobile device within the indoor environment.

By way of an initial example, a perceived routability constraint maycomprise all or part of a line (e.g., which may be straight, curved,solid, dashed, shaded, patterned, etc.) or possibly an object transition(e.g., which may be identifiable as an edge of some object or obstacle,a demarcation between two adjacent objects and/or obstacles, ademarcation between a physical obstacle that may hinder navigation andan adjacent region within an indoor environment that may permitnavigation there through, etc.).

With this in mind, in certain example implementations, at least aportion of the displayable image data may represent (e.g., viavector-based image data, raster-based image data, etc.) a plurality ofpixels (e.g., using pixel values, etc.) corresponding to at least aportion of a line or an object transition, and all or part of theplurality of pixels may be identified as one or more perceivedroutability constraints based, at least in part, on one or morethreshold values, thresholding tests, and/or the like.

For example, a threshold value may be based, at least in part, on aminimum pixilated line measurement (e.g., length measurement). Thus, forexample, if a plurality of adjacent pixels and/or other nearby pixelshaving similar pixel values exceeds a minimum pixilated line measurementthen all or part the plurality of adjacent pixels may be identified asone or more perceived routability constraints. Here, for example, if aminimum pixilated line measurement is equal to seventy (70) pixels thenall or part of a line or other grouping of seventy-one (71) or morepixels may be identified as one or more perceived routabilityconstraints. It should be understood that in certain implementations,various threshold values relating to lengths or widths, or other likeidentifiable groupings of pixels obtained from displayable image datamay indicate such thresholds based on a certain number of pixels (e.g.as illustrated preceding example), and/or some other applicablemeasurement techniques (e.g., millimeters, centimeters, inches, dots,some nominal grid unit, etc.).

In another example, a threshold value may be based, at least in part, ona minimum pixilated object transition measurement (e.g., line width orregion's depth measurement). Thus, for example, if a plurality ofadjacent pixels and/or some other like grouping of pixels having similarpixel values exceeds a minimum pixilated object transition measurementthen all or part of the plurality of pixels may be identified as one ormore perceived routability constraints. Here, for example, if a maximumpixilated noise measurement is equal to forty (40) then all or part of agrouping of forty-one (41) or more adjacent and/or otherwise groupedpixels may be identified as one or more perceived routabilityconstraints.

In yet another example, a threshold value may be based, at least inpart, on a maximum pixilated noise measurement (e.g., length and/orwidth measurement, or some other measurable grouping of pixels). Thus,for example, if a single pixel and/or some number of a plurality ofadjacent or neighboring pixels having similar pixel values exceeds themaximum pixilated noise measurement then such a single pixel may beidentified as a perceived routability constraint, or all or part of theplurality of pixels may be identified as one or more perceivedroutability constraints. Here, for example, if a maximum pixilated noisemeasurement is equal to fifteen (15) then all or part a grouping ofsixteen or more adjacent or neighboring pixels may be identified as oneor more perceived routability constraints.

As illustrated in the example above, a maximum pixilated noisemeasurement, as its name implies, may be implemented to serve as afilter to remove certain isolated, likely spurious, items that mayappear within displayable image data under certain conditions. With thisin mind, in accordance with certain implementations it may be beneficialto implement other image recognition and/or filtering techniques whichmight also help identify portions of the displayable image data asrepresenting a perceived routability constraint or not. Thus, forexample, various image filtering techniques may be used to remove orotherwise affect or identify displayable image data relating to dust orother noise or speckles that may appear in certain image data, e.g. asresult of scanning and/or other like processing image data. In anotherexample, various edge detection and/or contrast techniques may beimplemented to further refine certain image data and/or eliminatecertain image data. In still another example, it may be beneficial touse various image straightening techniques.

In certain example implementations it may be beneficial to perform someform of Optical Character Recognition (OCR) and/or the like to identifycertain added annotations which may then not be necessarily identifiedas a perceived routability constraint, e.g., clearly an added room name,such as “Conference Room”, within a portion of the displayable imagedata for electronic map of an office design is not intended to representsome physical obstacle within such a conference room. In anotherexample, color or shape analysis may be performed to identify certainannotations, etc.

In yet another example, in certain implementations it may be beneficialto perform some form of scale or normalization with regard to all orpart of the displayable image data. For example, in certain instances ascale may be corrected by identifying certain scale information, and/orpossibly identifying certain represented features within the displayableimage data that may be equated to a particular scale or size. Here forexample, in certain implementations, a doorway or a stair step, and/orsome other physical feature may be associated with certain buildingcodes or other like standards, and hence identifying such featureswithin an electronic map may allow for certain changes to one or moredimensional scales, which may improve the usefulness of the electronicmap and/or certain example techniques provided herein.

A measure of confidence may be associated with a perceived routabilityconstraint, e.g., based at least in part, on a number of adjacent pixelshaving similar pixel values (e.g., pixel values that fall within aparticular threshold range of pixel values), a comparison of the similarpixel values to pixel values of other surrounding and/or adjacentpixels, and/or the like or some combination thereof. It should berecognized in the examples above that various pixel values may beconsidered, e.g., including values identifying color or hue, intensity,and/or the like or some combination thereof. In certain implementations,for example, pixel values for some grouping of pixels may be consideredsimilar if they fall within some range of pixel values. In certainimplementations, for example, pixel values may be considered similar ifthey may be differentiated in some manner from various other adjacent ornearby/neighboring pixel values.

It should be understood that the examples presented herein relate tojust a few implementations that may be used to detect groupings ofpixels that relate to lines and/or object transitions within displayableimage data, and that other types of object detection, edge detection,transition detection, etc., may be implemented in certain other exampleimplementations. Likewise, in certain implementations, other tests thatdetect or identify regions without objects, regions without edges,regions without transitions, etc., may be implemented in like fashion tohelp identify one or more perceived routability constraints based, atleast in part, on the displayable image data.

It should be understood that in accordance with certain implementationsan array of pixels may comprise a plurality of pixels which may bearranged according to a particular pattern, more than one pattern, orpossibly according to some more random arrangement, e.g., depending upona particular display device upon which certain pixel values may berendered or otherwise processed in some manner to presented image thatmay be viewed by a person or possibly a machine. Thus, in accordancewith certain implementations, an array of pixels may comprise aplurality of pixels arranged in rows and columns. Hence, in suchimplementations adjacent pixels may share a common row and/or column,and/or may be arranged along some diagonal provided by applicable rowand column assignments. In accordance with certain otherimplementations, two pixels may be considered adjacent to one another ifthe two pixels share at least some portion of a common border corner orfacet, e.g. depending upon their various shapes. In certain instances,two pixels may be considered adjacent to one another if there are noother pixels between them. In accordance with certain implementations,accommodation may be made in certain implementations of the techniquesprovided herein to allow for different types of pixels arranged withinan array of pixels. Thus for example, two pixels of the same type may beadjacent to even though one or more other types of pixels may bearranged there between. Further, in certain instances, accommodation maybe made in certain implementations of the techniques provided herein toallow for a threshold number of pixels to be ignored or skipped whenconsidering whether two or more pixels may be adjacent to one another.Thus, for example, accommodation may be made for a certain number ofpixels that may appear within some line or pattern with dissimilar pixelvalues as a result of noise and/or other like distorting processes orinformation. In other example implementations, accommodation may be madefor certain number of pixels that may be inactive or otherwiseexperience certain operating conditions when considering whether two ormore pixels may be adjacent to one another. Hence, for example, it maybe acceptable in certain implementations for a thresholding and/or otherlike test for a particular line, object transition, noise grouping,etc., to permit, ignore, or otherwise skip certain pixel withindisplayable image data. Accordingly, in certain implementations, certaintests may allow for a range of acceptable conditions that may satisfysuch tests.

Further, it should be kept in mind that while some examples presentedherein show certain arrangements of pixel arrays and/or shapes ofindividual pixels, as with the other examples presented herein claimedsubject matter is not intended to be limited to such examples.

In certain example implementations additional information that may beprovided in metadata and/or other data files associated with displayableimage data, e.g. as part of an electronic map, may be used to furtherhelp identify or alternatively identify one or more perceivedroutability constraints and/or regions without perceived routabilityconstraints in accordance with certain further example implementations.Thus for example, certain CAD and/or other like drawings may includemetadata identifying certain objects within an electronic map by type,and in certain instances some of such identifiable types of objects mayor may not be identified as presenting a perceived routabilityconstraint, and/or a measure of confidence for such perceivedroutability constraint may be based on such additional information thatmay be available.

In certain instances, metadata may indicate that an object in anelectronic map relates to a key or other visual identifier (e.g.,annotating information, etc.) rather than an actual physical object thatmay affect movement of a mobile device within an indoor environment.Thus, such an object may be not identified as a perceived routabilityconstraint, and/or if identified as a perceived routability constraintits associated measure of confidence may be substantially low if notnonexistent due to the additional information that may be obtained andunderstood. Conversely, for example, metadata may indicate that aparticular object and electronic map relates to an installed officecubicle divider and/or some other like object that may or may not affectmovement of a mobile device within an indoor environment. Thus, such anobject may be identified as a perceived routability constraint and itsassociated measure of confidence may be based on such additionalinformation that may be obtained and understood.

With these examples and others in mind, in accordance with certainexample implementations, a perceived routability constraint maycorrespond to at least a portion of a line or an object transitionidentified within the displayable image data. Since a perceivedroutability constraint may or may not represent an actual physicalobstacle within an indoor environment, a measure of confidence may beassigned to or otherwise associated with the perceived routabilityconstraint. For example, a measure of confidence for a perceivedroutability constraint may be set using some form of a weight value thatmay be indicative of an estimated likelihood that a perceivedroutability constraint may or may not represent actual physical obstacleto navigation.

Accordingly, a weight value may be assigned to or otherwise associatedwith all or part of a line, an object transition and/or the likegrouping of pixels that may be identified within the displayable imagedata and identify as one or more perceived routability constraints.Thus, for example, an appropriate weight value may be associated withone or more pixel values and/or other like information that may beprovided within the displayable image data. Such a weight value may, forexample, identify and/or otherwise relate to an estimated likelihoodthat the perceived routability constraint represents at least part of anactual obstacle within the indoor environment capable of affectingmovement of the mobile device within the indoor environment. Thus, forexample, a weight value may take the form of a numerical value, e.g., aninteger between 0 and 10, a number between 0.00 and 1.00, a percentageand/or the like or some combination thereof. Here, for example, a weightvalue of 0 out of 10, or 0.00 out of 1.00 may indicate that there islittle if any estimated likelihood that the perceived routabilityconstraint represents an actual physical obstacle to navigation.Conversely, a weight value of 10 out of 10 or 1.00 out of 1.00 mayindicate that there is little if any doubt that the perceivedroutability constraint represents an actual physical obstacle tonavigation. Hence, assuming a linear scale, a weight value of 5 out of10, or 0.50 out of 1.00, may indicate that there is a 50% chance thatthe perceived routability constraint represents an actual physicalobstacle to navigation. Those skilled in the art will recognize thatother weighting techniques may be implemented in other implementationsand/or that such a likelihood logic may be reversed.

In accordance with certain example implementations, a propagation of atleast one particle within a particle filtering model and/or the like maybe based, at least in part, on a weight value associated with a portionof a line or an object transition. Thus, for example, a particle thatmay represent a propagated state of a mobile device with regard to anestimated trajectory may be propagated under certain conditions, e.g.,in the absence of a perceived routability constraint having somethreshold level of a measure of confidence in its path. In certainexample implementations, a particle filtering model and/or the like maymake use of a routability graph and/or other like information that mayrelate to feasible paths of navigation within an indoor environment.

Particle filter models have been used to model movement of objects intwo or three dimensions, for example. Here, over a time period a“particle” may transition from an initial position and/or state to asubsequent position and/or state in certain directions from the initialposition. Possible transitions to particular subsequent positions and/orstates may be modeled according to a probability model conditioned onthe initial position and/or state. In a particular example, thelikelihood that a particle would have a particular subsequent location,velocity and heading may be conditioned on an initial location, velocityand heading for the particle, and possibly, e.g., when applicable,considering information about one or more perceived routabilityconstraints as presented by the example techniques herein.

With this in mind, in accordance with certain other implementations, aperceived routability constraint may correspond to at least one “edge”that identified within a routability graph associated with at least aportion of the displayable image data. For example, a routability graphmay identify a plurality of feasible paths running between the locationsof particular nodes which may be interconnected by a plurality of“edges” (should not be confused with an edge of a line or an edge of anobject transition). One or more edges within a routability graph maycorrespond to a perceived routability constraint. For example, asillustrated and described in greater detail in subsequent sections, oneor more edges within a routability graph may cross over and or nearby aperceived routability constraint, such as a line or object transition,etc. Thus, for example, in certain implementations, in setting a measureof confidence for certain perceived routability constraints, anapplicable weight value may be associated with one or more applicableedges within a routability graph. Such a weight value may, for example,identify an estimated likelihood that the perceived routabilityconstraint represents at least part of an actual obstacle within theindoor environment capable of affecting a trajectory of the mobiledevice in a direction along or possibly parallel and offset from theparticular edge within a corresponding region of the indoor environmentas represented by a routability graph. In accordance with certainimplementations, a propagation of at least one particle within aparticle filtering model and/or the like may be affected based, at leastin part, on a weight value associated with an edge of routability graph.

In accordance with certain example implementations, such a computingplatform may be provided external to the mobile device and at least ameasure of confidence may be transmitted to one or more mobile devices.However, in accordance with certain other example implementations, sucha computing platform may be provided within the mobile device, which mayfurther estimate or otherwise assist in estimating a trajectory of themobile device, e.g., based, at least in part, on at least the measure ofconfidence.

In accordance with certain further example implementations a measure ofconfidence may be subsequently affected based, at least in part, onreceived positioning data for the mobile device and/or for one or moreother mobile devices, e.g., as result of their previous movement(s)therein within the indoor environment. Positioning data may beestimated, for example, using one or more signal-based positioningtechniques, one or more sensor-based positioning techniques, and/or thelike or some combination thereof. In certain instances, a positioningdata may be estimated, for example, using a particle filtering modeland/or the like.

In accordance with certain further example implementations, a measure ofconfidence may be affected based, at least in part, on an age and/orsome other time related information associated with all or part of theelectronic map. Thus, for example, a measure of confidence may bereduced over time in certain instances wherein various features(obstacles) may be selectively arranged within an indoor environment maybe altered, moved, added, etc., over some period of time. Accordingly,in certain implementations a measure of confidence may be reducedaccording to some linear and/or nonlinear function based on a passage oftime.

In accordance with certain example implementations, a mobile device maybe provisioned to receive a measure of confidence for at least oneperceived routability constraint identified based, at least in part, ondisplayable image data of an electronic map for an indoor environment,and estimate its trajectory with regard to the indoor environment based,at least in part, on the measure of confidence for the at least oneperceived routability constraint.

Attention is drawn now to FIG. 1, which is a schematic block diagramillustrating an example environment 100 that includes a computing device102 and a mobile device 104 and one or more other mobile devices 104-n,one or more of which may provide or otherwise support mobile devicepositioning based, at least in part, on one or more perceivedroutability constraints that may or may not affect movement of a mobiledevice within an indoor environment 125, in accordance with an exampleimplementation.

As shown, computing device 102 comprises an apparatus 112 to provide orotherwise support mobile device positioning based, at least in part, ona one or more perceived routability constraints that may or may notaffect movement of a mobile device 104 within indoor environment 125.Apparatus 112 may represent one or more computing platforms that maycommunicate with one or more other resources (devices) 130, one or moreother mobile devices 104-n, either directly and/or indirectly, e.g. viaone or more network(s) 120. Apparatus 112 may communicate with mobiledevice 104, either directly and/or indirectly, the latter which isillustrated using network(s) 120 and wireless communication link 122.While computing device 102 happens to be illustrated in this example asbeing located outside of indoor environment 125, it should be recognizedthat in certain other implementations, all or part of computing device102 and/or apparatus 112 may be located within indoor environment 125.

Network(s) 120 may comprise one or more communication systems and/ordata networks having various interconnected devices supportingcommunication between computing device 102, one or more other mobiledevices 104-n, and one or more other resources (devices) 130. Asmentioned, network(s) 120 may further support communication betweencomputing device 102 and mobile device 104, and/or between computingdevice 102 and one or more other mobile devices 104-n. For example,communication between computing device 102 and mobile device 104 mayallow for certain data and/or instructions to be exchanged therebetween.

As used herein a “mobile device” may represent any electronic devicethat may be moved about either directly or indirectly by a user withinan indoor environment and which may communicate with one or more otherdevices via one or more wired and/or wireless communication links. Someexamples include a cell phone, a smart phone, a computer (e.g., apersonal computer such as a laptop computer, tablet computer, a wearablecomputer, etc.), a navigation aid, a tracking device, a digital bookreader, a gaming device, music and/or video player device, a camera, amachine, a robot, etc.

Other resources (devices) 130 may represent one or more computingplatforms from which computing device 102 and/or mobile device 104 mayreceive certain data files and/or instructions, and/or to whichcomputing device 102 and/or mobile device 104 may provide certain datafiles and/or instructions. For example, in certain instances, all orpart of an electronic map and/or the like may be received by computingdevice 102 and/or mobile device 104 from one or more other resources(devices) 130. For example, in certain instances, all or part of a setof instructions for use in apparatus 112 and/or apparatus 110 may bereceived from other resources (devices) 130.

Example environment 100 further includes a satellite positioning system(SPS) 150 which may transmit one or more SPS signals 152 to mobiledevice 104. SPS 150 may, for example, represent one or more GNSS, one ormore regional navigation satellite systems, and/or the like or somecombination thereof. Additionally, one or more terrestrial-basedpositioning systems may be provided as represented by exampletransmitting device(s) 140 capable of transmitting one or more wirelesssignals 142 all or some of which may be used for signal-basedpositioning. Thus for example, transmitting device(s) 140 may representa wireless access point, a base station, a repeater (e.g., for a femtocell, a pico cell, etc., in support of a communication network), adedicated beacon transmitting device, a personal area network (PAN)device, a Blue tooth device, just to name a few examples, which haveknown or otherwise identifiable positions. Accordingly, in someimplementations, certain example transmitting devices 140 may alsocomprise a capability to receive wireless (and/or wired) signals. Incertain example implementations, a transmitting device 140 may befurther capable of connecting to network(s) 120. SPS signals 152 and/orwireless signals 142 may, at times, be acquired by mobile device 104 andused to estimate its position.

As further illustrated example indoor environment 125 may comprise oneor more obstacles 160 that may be arranged on a permanent and/ortemporary, and/or possibly recurring basis, within indoor environment125. Obstacles 160 may represent various physical features that may bepart of an indoor environment (e.g., a man-made and/or naturalstructure). For example, obstacles 160 may represent all or part of: oneor more walls, one or more ceilings, one or more floors, one or morewindows or doors or doorways, one or more elevators, one or moremachines, one or more partitions, certain furniture, a staircase, aladder, an atrium, a cofferdam, a structural member, an non-structuralmember, a planter, a display case, a work of art, a catwalk, a queuingdivider, and/or the like or some combination thereof.

Attention is drawn next to FIG. 2, which is a schematic block diagramillustrating certain features of an example computing platform 200 in acomputing device 102 to provide or otherwise support mobile devicepositioning based, at least in part, on one or more perceivedroutability constraints, in accordance with an example implementation.

As illustrated computing platform 200 may comprise one or moreprocessing units 202 to perform data processing (e.g., in accordancewith the techniques provided herein, as part of apparatus 112, etc.)coupled to memory 204 via one or more connections 206. Processingunit(s) 202 may, for example, be implemented in hardware or acombination of hardware and software. Processing unit(s) 202 may berepresentative of one or more circuits configurable to perform at leasta portion of a data computing procedure or process. By way of examplebut not limitation, a processing unit may include one or moreprocessors, controllers, microprocessors, microcontrollers, applicationspecific integrated circuits, digital signal processors, programmablelogic devices, field programmable gate arrays, or the like, or anycombination thereof.

Memory 204 may be representative of any data storage mechanism. Memory204 may include, for example, a primary memory 204-1 and/or a secondarymemory 204-2. Primary memory 204-1 may comprise, for example, a randomaccess memory, read only memory, etc. While illustrated in this exampleas being separate from the processing units, it should be understoodthat all or part of a primary memory may be provided within or otherwiseco-located/coupled with processing unit(s) 202, or other like circuitrywithin computing platform 200. Secondary memory 204-2 may comprise, forexample, the same or similar type of memory as primary memory and/or oneor more data storage devices or systems, such as, for example, a diskdrive, an optical disc drive, a tape drive, a solid state memory drive,etc.

In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, a non-transitorycomputer readable medium 270. Memory 204 and/or non-transitory computerreadable medium 270 may comprise instructions 272 associated with dataprocessing, e.g., in accordance with the techniques and/or exampleapparatus 112 (FIG. 1) and/or all or part of example process 1100 (FIG.11), as provided herein.

Computing platform 200 may, for example, further comprise one or morecommunication interface(s) 208. Communication interface(s) 208 may, forexample, provide connectivity to network(s) 120, mobile device 104, oneor more other mobile devices 104-n, and/or other resources (devices) 130(FIG. 1), e.g., via one or more wired and/or wireless communicationlinks. As illustrated in this example, communication interface(s) 208may comprise one or more receiver(s) 210, one or more transmitter(s)212, and/or the like or some combination thereof. Communicationinterface(s) 208 may implement one or more communication protocols asmay be required to support one or more wired and/or wirelesscommunication links.

Processing unit(s) 202 and/or instructions 282 may, for example, provideor otherwise be associated with one or more signals that may be storedin memory 204 from time to time, such as: instructions 272; apparatus112; one or more electronic maps 220; one or more forms of displayableimage data 222 (e.g. associated with one or more electronic maps 220);one or more perceived routability constraints 224 (e.g., associated withcertain displayable image data, and/or a corresponding portion of aroutability graph 248, such as, one or more nodes 250 and/or one or moreedges 252 within a routability graph 248); one or more measures ofconfidence 226 (e.g., associated with one or more perceived routabilityconstraints 224); one or more forms of vector-based image data 230; oneor more forms of raster-based image data 232; one or more forms of pixelinformation 234 (e.g., one or more pixel values associated with portionof displayable image data 222); one or more identified lines 236; one ormore identified object transitions 238; one or more threshold values240; one or more weight values 242 (e.g. associated with one or moreperceived routability constraints 224 and/or one or more measures ofconfidence 226); one or more estimated likelihoods 244 (e.g.,probability values and/or the like associated with one or more measuresof confidence 226); one or more routability graphs 248 (e.g., associatedwith all or part of one or more electronic maps 220); one or more nodes250; one or more edges 252; positioning data 254 (e.g., associated withone or more mobile devices traversing through all or part of an indoorenvironment); one or more ages 256 (e.g., time-related informationassociated with one or more electronic maps 220); and/or the like orsome combination thereof.

Attention is drawn next to FIG. 3, which is a schematic block diagramillustrating certain features of an example computing platform 300 in amobile device 104 to provide or otherwise support mobile devicepositioning based, at least in part, on one or more perceivedroutability constraints, in accordance with an example implementation.

As illustrated computing platform 300 may comprise one or moreprocessing units 302 to perform data processing (e.g., in accordancewith the techniques provided herein, and/or apparatus 110, etc.) coupledto memory 304 via one or more connections 306. Processing unit(s) 302may, for example, be implemented in hardware or a combination ofhardware and software. Processing unit(s) 302 may be representative ofone or more circuits configurable to perform at least a portion of adata computing procedure or process. By way of example but notlimitation, a processing unit may include one or more processors,controllers, microprocessors, microcontrollers, application specificintegrated circuits, digital signal processors, programmable logicdevices, field programmable gate arrays, or the like, or any combinationthereof.

Memory 304 may be representative of any data storage mechanism. Memory304 may include, for example, a primary memory 304-1 and/or a secondarymemory 304-2. Primary memory 304-1 may comprise, for example, a randomaccess memory, read only memory, etc. While illustrated in this exampleas being separate from the processing units, it should be understoodthat all or part of a primary memory may be provided within or otherwiseco-located/coupled with processing unit(s) 302, or other like circuitrywithin mobile device 104. Secondary memory 304-2 may comprise, forexample, the same or similar type of memory as primary memory and/or oneor more data storage devices or systems, such as, for example, a diskdrive, an optical disc drive, a tape drive, a solid state memory drive,etc.

In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, a non-transitorycomputer readable medium 370. Memory 304 and/or non-transitory computerreadable medium 370 may comprise instructions 372 associated with dataprocessing, e.g., in accordance with the techniques and/or exampleapparatus 110 (FIG. 1) and/or all or part of one or more exampleprocesses 1100 (FIG. 11) and/or 1200 (FIG. 12), as provided by way ofexample herein.

Computing platform 300 may, for example, further comprise one or morecommunication interface(s) 308. Communication interface(s) 308 may, forexample, provide connectivity to network(s) 120, computing device 102,one or more other mobile devices 104-n, other resources (devices) 130,and/or one or more transmitting devices 140 (FIG. 1), e.g., via one ormore wired and/or wireless communication links. As illustrated herecommunication interface(s) 308 may, for example, comprise one or morereceivers 310, one or more transmitters 312, and/or the like or somecombination thereof. Communication interface(s) 308 may implement one ormore communication protocols as may be required to support one or morewired and/or wireless communication links. Communication interface(s)308 may, in certain example instances, further comprise one or morereceivers capable of receiving wireless signals 142 from one or moretransmitting devices 140 associated with one or more terrestrial-basedpositioning systems. Further, in certain example instances, mobiledevice 104 may comprise an SPS receiver 318 capable of receiving andprocessing SPS signals 152 in support of one or more signal-basedpositioning capabilities.

In accordance with certain example implementations, communicationinterface(s) 208, communication interface(s) 308, and/or other resourcesin network(s) 120 may, for example, be enabled for use with variouswireless communication networks such as a wireless wide area network(WWAN), a wireless local area network (WLAN), a wireless personal areanetwork (WPAN), and so on. The term “network” and “system” may be usedinterchangeably herein. A WWAN may be a Code Division Multiple Access(CDMA) network, a Time Division Multiple Access (TDMA) network, aFrequency Division Multiple Access (FDMA) network, an OrthogonalFrequency Division Multiple Access (OFDMA) network, a Single-CarrierFrequency Division Multiple Access (SC-FDMA) network, and so on. A CDMAnetwork may implement one or more radio access technologies (RATs) suchas cdma2000, Wideband-CDMA (W-CDMA), Time Division Synchronous CodeDivision Multiple Access (TD-SCDMA), to name just a few radiotechnologies. Here, cdma2000 may include technologies implementedaccording to IS-95, IS-2000, and IS-856 standards. A TDMA network mayimplement Global System for Mobile Communications (GSM), DigitalAdvanced Mobile Phone System (D-AMPS), or some other RAT. GSM and W-CDMAare described in documents from a consortium named “3rd GenerationPartnership Project” (3GPP). Cdma2000 is described in documents from aconsortium named “3rd Generation Partnership Project 2” (3GPP2). 3GPPand 3GPP2 documents are publicly available. A WLAN may include an IEEE802.11x network, and a WPAN may include a Bluetooth network, an IEEE802.15x, for example. Wireless communication networks may includeso-called next generation technologies (e.g., “4G”), such as, forexample, Long Term Evolution (LTE), Advanced LTE, WiMAX, Ultra MobileBroadband (UMB), and/or the like. Additionally, communicationinterface(s) 208 and/or communication interface(s) 308 may furtherprovide for infrared-based communications with one or more otherdevices.

Mobile device 104 may, for example, further comprise one or moreinput/output units 314. In certain implementations, input/output units314 may be used for receiving perceived routability constraint data. Incertain implementations, input/output units 314 may be used to presentestimated positioning information to a user of mobile device, and suchestimated positioning information may be based, at least in part, on atrajectory of the mobile device within the indoor environment, e.g., ascalculated by the various example techniques herein. Here, an exampleoutput unit may comprise a speaker or headphone jack that may be used topresent estimated positioning information to user via one or more of anaudio signal signals. In certain examples, example output unit maycomprise a display or other like visual indicator that may be used topresent estimated positioning information to a user in some displayableformat, e.g., a trajectory overlaid onto part of electronic map,particularly orientated directional arrows, etc. Input/output units 314may represent one or more devices or other like mechanisms that may beused to receive inputs from and/or provide outputs to one or more otherdevices and/or a user of mobile device 104. Thus, for example,input/output units 314 may comprise various buttons, switches, a touchpad, a trackball, a joystick, a touch screen, a microphone, a camera,and/or the like, which may be used to receive one or more user inputs.In certain instances, input/output units 314 may comprise variousdevices that may be used in producing a visual output, an audibleoutput, and/or a tactile output for a user. In one exampleimplementation, input/output units 314 may comprise a display capable ofrendering all or part of displayable image data 222 and/or the like.

Mobile device 104 may, for example, comprise one or more sensors 316.For example, sensor(s) 316 may represent one or more inertial sensors,one or more environmental sensors, etc., which may be useful indetecting aspects of the environment 100 and/or mobile device 104. Thusfor example, sensor(s) 316 may comprise one or more accelerometers, oneor one or more gyroscopes or gyrometers, one or more magnetometersand/or the like, one or more barometers, one or more thermometers, etc.Further, in certain instances sensor(s) 316 may comprise one or moreinput devices such as a microphone, a camera, a light sensor, etc.

Processing unit(s) 302 and/or instructions 372 may, for example, provideor otherwise be associated with one or more signals that may be storedin memory 304 from time to time, such as: instructions 372; apparatus110; one or more estimated trajectories 320; one or more particlefiltering models 322; one or more electronic maps 220; one or more formsof displayable image data 222; one or more perceived routabilityconstraints 224; one or more measures of confidence 226; one or moreforms of vector-based image data 230; one or more forms of raster-basedimage data 232; one or more forms of pixel information 234; one or moreidentified lines 236; one or more identified object transitions 238; oneor more threshold values 240; one or more weight values 242; one or moreestimated likelihoods 244; one or more routability graphs 248; one ormore nodes 250; one or more edges 252; positioning data 254; one or moreages 256; and/or the like or some combination thereof. While some theexample data and/or instructions as illustrated in FIG. 3 share the samereference numbers as example data and/or instructions as illustrated inFIG. 2, it should be kept in mind that in certain instances all or partof such example data and/or instructions may be distinctly different.

Attention is drawn next to FIG. 4, which is a diagram 400 showingcertain example physical features of an indoor environment that may berepresented in an electronic map and which may act as actual obstaclesto movement of a person (e.g. carrying mobile device 104) within theindoor environment, in accordance with an example implementation. By wayof example, diagram 400 may represent a visual presentation of all orsome of the information that may be provided in one or more data filesand/or instructions associated with an electronic map and/or which anelectronic map may be based, at least in part, for indoor environment.

As presented herein, diagram 400 is intended to illustrate that featureswithin an indoor environment relate to actual physical objects havingthree-dimensional characteristics, e.g., for example, in diagram 400various walls and support structures are illustrated as havingnontrivial cross-sections. With this in mind, example diagram 400presents several features that may constrain the movement of a personcarrying a mobile device in some manner while navigating within theindoor environment. Diagram 400 depicts four rooms, 402-1, 402-2, 402-3,and 402-4, which have openings to a common hallway 404. Here forexample, room 402-3 is connected to hallway 404 via doorway or otherlike opening 406. As further illustrated in addition to the walls orother like partitions illustrated in diagram 400, a structural member410 is also depicted near the center of diagram 400. Hence, as would beexpected, a person carrying a mobile device may be able to navigatethrough part of hallway 404 and possibly enter one or more of the roomsshown via their doorway and/or other like openings. Further, as would beexpected, the person carrying a mobile device may be unable to navigatethrough one of the walls and/or other like partitions, and/or structuralmember 410. It should be kept in mind that diagram 400 is intended justto provide a simple example of an indoor environment the form of anoffice building.

Attention is drawn next to FIG. 5, which presents an example diagram 500showing the features of diagram 400 along with example correspondingdisplayable image data in the form of lines 502 illustrated as overlaidwith their represented physical features, in accordance with an exampleimplementation. As can be seen in this example, while lines 502 used indiagram 500 may be aligned in some fashion with the physical featuresthey represent, some information regarding scale and/or width, etc., maybe lost since the lines tend to be thinner than some of thecross-sections of their represented physical features.

Attention is drawn next to FIG. 6, which is a diagram 600 showing anexample depiction of displayable image data based on the representativelines 502 from diagram 500. As further illustrated, in certainimplementations displayable image data as in diagram 600 may be furtherintentionally annotated and/or otherwise altered in some manner based,at least in part, additional displayable image data that may or may notbe related to the represented physical features. Here, for example,diagram 600 has been further intentionally annotated include a compassorientation key 602. In other implementations, displayable image datamay include additional information such as building and/or room names,or other identifying information that may be of use to a person ormachine reading or otherwise accessing the displayable image data. Incertain example implementations, all or part of the displayable imagedata associated with diagram 600 may comprise vector-based image data.Hence, as is known, vector-based image data may support various levelsof scaling during rendering of an image.

Attention is drawn next to FIG. 7, which is a diagram 700 showing anexample depiction of displayable image data as in diagram 600 but whichis further illustrated as having certain example unintended additionaldisplayable image data and/or distorted displayable image data that mayor may not be related to actual physical features of the indoorenvironment and/or any intentional annotation or other like addedinformation, in accordance with an example implementation.

By way of example, diagram 700 may represent some form of displayableimage data that may be less precise than vector-based image data and/orother like drawing/renderings, as a result of one or more dataconversion processes and/or scanning or photocopying processes. Thus,for example, diagram 600 (FIG. 6) may represent a more preciseelectronic map (e.g., which may comprise vector-based image data and/orthe like) and diagram 700 may represent a less precise, possibly morenoisy, corresponding electronic map (e.g., which may compriseraster-based image data and/or the like). Hence, diagram 700 mayrepresent a scanned version, or possibly a converted file format ofdiagram 600, etc.

As illustrated, diagram 700 may comprise spurious attributes or otherlike noise as represented by items 702. As may be appreciated, items 702do not relate to any represented physical features of the indoorenvironment. Nonetheless, in certain implementations, certain items suchas items 702 may be identified as perceived routability constraintswhich may affect trajectory estimation's and/or other like positioningdata. However, as presented by the various techniques herein, it may bepossible to reduce or avoid the impact of such perceived routabilityconstraints based, at least in part, on a measure of confidence that maybe identified. Thus, for example, in accordance with certainimplementations certain items, such as example items 702, may not beidentified as a perceived routability constraint in the first place,unless they meet certain threshold requirements (e.g., relating toaspects such as length, width, number of adjacent or grouped pixels,similar/dissimilar pixel values, additional knowledge that may beprovided by previously received positioning data for one or more mobiledevices within the indoor environment, etc.).

Other example distortions or other like changes that may occur due tovarious processing techniques are illustrated in diagram 700 as well.For example, item 704 in diagram 700 illustrates that the full shape ofstructural member 410 (e.g., see diagrams 400 and 500) may not have beenconverted properly at some information has been lost. For example, items706, 708 and 710 illustrate that line widths may be altered as result ofbeing converted. Here for example, item 706, which represents one of the(closed) doors leading into a room, appears thinner in width than theother (closed) door leading into the same room. Here for example, item706 which represents a wall appears thinner in width than the otherrelated walls. Conversely, for example, item 710 which also represents awall appears thicker in width than the other related walls.

It should be recognized that the foregoing present just a few examplesand that in certain implementations various forms of data conversionand/or other like processes may result in various other items,alterations, aberrations, etc. In certain other instances, it may bepossible for such data conversions and/or other like processes to notadd any deleterious items to displayable image data, indeed, in certainimplementations a resulting displayable image data may actuallyrepresent an improvement over previous form based on techniques thatcleanup remove noise and/or seek to make other changes.

Attention is drawn next to FIG. 8, which is a diagram showing an examplepixel array 800 that may be used to display displayable image data, inaccordance with an example implementation. In this example pixels areillustrated as having a square shape and arranged in pixel array 800 anduniform rows and columns. The pixel values used to render a display viapixel array 800 are illustrated as belonging to four differentcategories. The first category of pixel values are marked with a period(“.”), the second category of pixel values are marked with a letter “a”,the third category of pixel values are marked with a letter “b”, and thefourth category of pixel values are marked with a letter “c”. Thus, forexample, pixel values within a given category may be similar in somemanner with each other and dissimilar in some distinguishing manner topixel values and other categories. With this in mind, for example, thefirst category of pixel values (e.g., such as pixel 802) may represent abackground color, intensity, etc.); the second category of pixel values(e.g., the letter “a” pixels may represent an intended line or an objecttransition that may have a particular color, shade, intensity, etc.);the third category of pixel values (e.g., the letter “b” pixels mayrepresent an unintended item that may have a particular color, shade,intensity, etc., which may or may not be different from that of theletter “a” or “c” pixels); and/or the fourth category of pixel values(e.g., the letter “c” pixels may represent an unintended item (e.g.,noise) that may have a particular color, shade, intensity, etc., whichmay or may not be different from that of the letter “a” or “b” pixels).

As illustrated, the letter “a” pixels are arranged adjacent and as agroup appear to form a line having width 806-1 and at least a lengthequal to or greater than length 804-1. Thus, in certain exampleimplementations, such letter “a” pixels may be compared to one or morethreshold values to determine whether or not they represent a perceivedroutability constraint. Here, for example, one or more threshold valuesmay be based on a minimum pixilated line measurement which may becompared to length 804-1. Here, for example, one or more thresholdvalues may be based on a minimum pixilated object transition measurementwhich may be compared to width 806-1. Further, for example, one or morethreshold values may be based on a maximum pixilated noise measurementthat may be compared to a number of adjacent otherwise grouped letter“a” pixels. In this example, it is assumed there for that the exampleletter “a” pixels are identified as a perceived routability constraint.In certain example implementations a measure of confidence associatedwith such perceived routability constraint may be affected (e.g., atleast initially set higher to represent an estimated likelihood that itmay represent an obstacle) based on the number of threshold test(s) thatmay be satisfied.

However, in this example, the same may not be true for letter “b” and/orletter “c” pixels. Here, for example, letter “b” pixels may be comparedto one or more threshold values to determine whether or not theyrepresent a perceived routability constraint. Thus, for example, one ormore threshold values may be based on a minimum pixilated linemeasurement which may be compared to length 804-2. Here, for example,one or more threshold values may be based on a minimum pixilated objecttransition measurement which may be compared to width 806-2. Further,for example, one or more threshold values may be based on a maximumpixilated noise measurement that may be compared to a number of adjacentotherwise grouped letter “b” pixels. In this example, it is assumed thatthe example letter “b” pixels may satisfy at least one threshold testand as such may be identified as a perceived routability constraint. Forexample, let us assume that the example letter “b” pixels, of whichthere are four (4), may satisfy at least one threshold test based on amaximum pixilated noise measurement that is set to three (3). However,in certain example implementations a measure of confidence associatedwith such perceived routability constraint may be affected (e.g., atleast initially set low to represent an estimated likelihood that it maynot represent an obstacle) based on such a barely-satisfied thresholdtest(s) and/or possibly one or more other failed threshold tests.

In this example, letter “c” pixels may be compared to one or morethreshold values to determine whether or not they represent a perceivedroutability constraint. Thus, for example, one or more threshold valuesmay be based on a minimum pixilated line measurement which may becompared to grouped pixels 808 which have a nominal length of one (1)pixel. Here, for example, one or more threshold values may be based on aminimum pixilated object transition measurement which may be compared togrouped pixels 808 which have a width of 806-2. Further, for example,one or more threshold values may be based on a maximum pixilated noisemeasurement that may be compared to a number of adjacent otherwisegrouped pixels 808, of which there are two (2). In this example, it isassumed that the example letter “c” pixels may fail to satisfy all ofthe threshold test(s) that may be applied, and as such may not beidentified as a perceived routability constraint.

It should be understood that, while the example pixel array 800illustrates only a small set of pixels, perceived routabilityconstraints in the form of lines, object transitions, etc., may relateto groupings of hundreds or thousands of pixels. Indeed, as may beappreciated, a measure of confidence associated with a perceivedroutability constraint may be set to represent a higher estimatedlikelihood that the perceived routability constraint represents anactual physical obstacle to navigation as the grouping of pixelsincreases and/or peers to adhere or otherwise conform to certainexpected design criteria. As such, in certain examples, one or moremeasures of confidence for one or more perceived routability constraintsfor a line, an object transition, or some other grouping of pixels mayrelate to its length and/or width, or possibly its a direction, orshape, pattern, color etc., relative to certain other lines, objecttransitions, or other groupings of pixels.

In certain implementations, one or more measures of confidence for oneor more perceived routability constraints for a line, an objecttransition, or some other grouping of pixels may relate to itsarrangement with regard to certain other lines, object transitions, orother groupings of pixels. Here, for example, measures of confidence forperceived routability constraints associated with interconnected lines(e.g., representing the walls defining and/or located within an indoorenvironment) may reflect a strong or high estimated likelihood that suchperceived routability constraints represents an actual physicalobstacles to navigation. Conversely, for example, a measure confidencefor a perceived routability constraint that appears to standalone fromother perceived routability constraints (e.g., representing a structuralmember 410 (FIG. 5)) may, in certain instances, at least initiallyreflect a relatively weaker or lower estimated likelihood that suchperceived routability constraint represents an actual physical obstaclesnavigation. Indeed, it should be recognized that while a structuralmember 410 clearly represents a physical obstacle that a person wouldnavigate around, in certain implementations such a minor deviation in atrajectory may not affect an estimated overall trajectory. Nonetheless,in certain implementations it may be beneficial to identify that suchperceived routability constraints (here, e.g., structural members) maybe common to an indoor environment (e.g., having similar shapes,possibly arranged according to some particular pattern, etc.), and henceit may be beneficial to set the applicable measures of confidence toreflect that such perceived routability constraints may stronglyrepresent physical obstacles navigation.

Attention is drawn next to FIG. 9, which is a diagram 900 showing anexample depiction of displayable image data as in FIG. 6 and acorresponding routability graph which may conform to one or moreperceived routability constraints based, at least in part, on a measureof confidence with regard to features in the electronic map and/orcertain edges in the routability graph, in accordance with an exampleimplementation.

In this example, the routability graph is illustrated based on atwo-dimensional grid pattern comprising a plurality of grid points ornodes such as nodes 902-1 and 902-2, which are neighboring nodes to oneanother and as illustrated connected by an edge 904-1. As will beappreciated, in this example, horizontal and vertical aligned edges willshare a common edge length, and similarly, and diagonal aligned edges,e.g., such as edge 904-2, will also share another common edge lengthwhich will be longer than the common edge length of the horizontal andvertical aligned edges. In other implementations, different shaped gridpatterns may be used, which may or may not be uniform and one or moreits dimensions.

As can be seen the routability graph is intended to represent feasiblepaths for a person carrying a mobile device within the representativeindoor environment. Thus, for example, a trajectory of a mobile devicemay be estimated as traveling from one node to an adjacent node via aparticular edge and interconnecting nodes.

For example, reference is made next to FIG. 10, which is a diagram 1000showing an example depiction of displayable image data and routabilitygraph as in diagram 900, and further illustrating a simulated trajectory1002 and a possible estimated trajectory 1004 of a mobile device withrespect to the routability graph in the example indoor environment, inaccordance with an example implementation.

Returning back to diagram 900 in FIG. 9, various items, intentionallyadded and/or unintentionally added such as those illustrated in FIGS. 6and 7 may be identified, at least initially, as perceived routabilityconstraints. For example, region 906 shows that all or part of one ormore of the illustrated opened and/or closed doors and/or their pivotingmovement lines may be identified as one or more perceived routabilityconstraints. For example, region 908 shows that all or part of a compassorientation key may be identified as one or more perceived routabilityconstraints. For example, region 910 shows that all or part of acorrupted or otherwise unintentionally affected line width or objecttransition width, etc., may not be identified as presenting one or moreperceived routability constraints. For example, region 912 shows thatall or part of a corrupted or otherwise unintentionally affected linelength or object transition length, etc., may not be identified aspresenting one or more perceived routability constraints.

Example diagram 1000, in FIG. 10, illustrates the routability graphwhich correctly accounts for perceived routability constraints, whencompared to example diagram 900. For example, in diagram 1000 a measureof confidence associated with all or part of illustrated opened and/orclosed doors and/or their pivoting movement lines, and/or the overlyingnodes and edges in the routability graph may be initially set and/orsubsequently affected to reduce an estimated likelihood that there is acorresponding physical feature that may act as an obstacle tonavigation. Hence, as illustrated by the routability graph in diagram1000 it is possible for an estimated trajectory to allow for navigationthrough the doors in region 906 (FIG. 9), e.g., assuming that the personmay open and close the doors as needed. Similarly, for example, one ormore perceived routability constraints based on all or part of a compassorientation key in region 908 (FIG. 9), may be initially set and/orsubsequently affected to reduce an estimated likelihood that there is acorresponding physical feature that may act as an obstacle tonavigation.

Further, as illustrated in example diagram 1000, additional perceivedroutability constraints may be identified and/or measures of confidenceassociated there with may be affected over time based on receivedpositioning data from one or more mobile devices in an effort to theroutability graph and/or electronic map with regard to example defectsas illustrated in regions 910 and 912 of diagram 900. Thus, for example,a routability graph and/or corresponding portions of the displayableimage data electronic map may be refined or otherwise affected to a moreprecisely identify perceived routability constraints and said applicablemeasures of confidence thereto with regard to the actual dimensions ofphysical obstacles and/or other features that may be part of an indoorenvironment.

The routability graph in example diagram 1000 depicts a fairly refinedset of nodes and edges that may be associated with feasible pathsthrough the indoor environment. Thus, for example, nodes and/or edgesthat overlap or cross certain obstacles have been removed from the gridpattern leaving only those associated with feasible paths. It should beunderstood that in accordance with certain implementations decisions asto whether a particular note and/or edge may or may not be removed maybe based, at least in part, on the perceived routability constraintsidentified within the displayable image data, and more particularly,based on the corresponding measures of confidence associated with thoseperceived routability constraints.

In certain example implementations, a measure of confidence may beassociated with a perceived routability constraint identified in thedisplayable image data. For example, a plurality of pixels associatedwith certain lines and/or object transitions may be identified in thedisplayable image data as perceived routability constraints. For eachperceived routability constraint, a measure of confidence may bedetermined. A measure of confidence may, for example, relate to anestimated likelihood that a perceived routability constraint may or maynot represent an actual obstacle to navigation.

An estimated likelihood that a perceived routability constraint may ormay not represent an actual obstacle to navigation may take into accounta variety of factors or other considerations that may be received fromelectronic map and/or other information that may be received, such as,e.g. positioning data from the mobile device and/or one or more othermobile devices as result of having been carried by person navigatingwithin the indoor environment. For example, an estimated likelihood thatthe perceived routability constraint may represent an actual obstaclemay be based, at least in part, on the number of pixels and/or someother aspect associated with a grouping and/or other like arrangement ofpixels, and/or certain characteristics of pixel values associated therewith. For example, an estimated likelihood that a perceived routabilityconstraint represents an actual obstacle may be increased as the numberof pixels, etc., associated with the perceived routability constraintincreases. Thus, for example, an estimated likelihood may be increasedas a result of identifying lines and/or object transitions that arelonger in length and/or wider in width. Thus, for example, an estimatedlikelihood may be reduced as result of identifying lines and/or objecttransitions that are relatively shorter in length and/or narrower inwidth.

Further, in certain examples, an estimated likelihood may be increasedfor lines and/or object transitions that appear to adhere to certaincommon and/or otherwise expected characteristics, e.g., directions,shapes, patterns, colors, shades, layer(s) (e.g., in the displayableimage data), and/or the like or some combination thereof. Conversely, anestimated likelihood may be decreased for lines and/or objecttransitions that do not appear to adhere to certain common and/orotherwise expected characteristics. By way of example, in certainimplementations the arrangement of certain walls and other obstacleswithin an indoor environment may adhere to certain design criteria, suchas, e.g., one or more geometric patterns wherein the resulting rooms andother indoor spaces have fairly straight walls, possibly parallelopposing walls, etc. In certain other implementations, indoorenvironment may adhere to some other design criteria wherein certainwalls may be curved in some manner, etc. Accordingly, an estimatedlikelihood may be increased based, at least in part, on a determinationthat the perceived routability constraint (here, e.g., a line and/orobject transition, etc.) appears to adhere to some identified designcriteria along with other perceived routability constraints. Conversely,an estimated likelihood may be decreased based, at least in part, on adetermination that the perceived routability constraint does not appearto adhere to some identified design criteria that may be exhibited byother perceived routability constraints.

In accordance with certain implementations, a measure of confidence withregard to a perceived routability constraint may be represented by oneor more weight values that may be associated with the perceivedroutability constraint. Thus for example, waiting values may beassociated with one or more pixel values within displayable image data.Thus, for example, in estimating a trajectory of a mobile device withinthe indoor environment, one or more weight values that may be associatedwith the perceived routability constraint may be considered indetermining whether a candidate trajectory may be possible or possiblyinhibited due to one or more obstacles to navigation.

In accordance with certain other implementations, a measure ofconfidence with regard to a perceived routability constraint may berepresented by one or more weight values that may be associated with oneor more nodes and or edges of a routability graph that may beoverlapping, crossing, near, adjacent, and/or otherwise associated withthe perceived routability constraint.

Thus, for example, in estimating a trajectory of a mobile device withinthe indoor environment, one or more weight values that may be associatedwith an edge of a routability graph crossing over and/or near by aperceived routability constraint may be considered in determiningwhether a candidate trajectory along or nearby such edge may be possibleor possibly inhibited due to one or more obstacles to navigation.

As previously mentioned, in certain implementations, a particlefiltering model and/or the like may be implemented in which particleswhich may be propagated forward in time may represent candidatetrajectories. Here, for example, one or more weight values and/or otherlike information associated with a perceived routability constraintand/or an applicable edge in a routability graph may affect the particlefiltering model accordingly.

Attention is drawn next to FIG. 11, which is a flow diagram illustratingan example process 1100 that may be implemented in whole or in part in acomputing platform of a computing device and/or a mobile device toprovide or otherwise support mobile device positioning based, at leastin part, on one or more perceived routability constraints that may ormay not affect movement of a mobile device within an indoor environment,in accordance with an example implementation.

At example block 1102, an electronic map for an indoor environment maybe received. Here, for example, an electronic map may comprise one ormore data files having data and/or instructions therein. For example,electronic map may comprise one or more forms of displayable image data,and/or may comprise instructions that may be used to determine one ormore forms of displayable image data. In certain implementations, all orpart of an electronic map may be received from a memory, and/or one ormore external devices, and/or the like or some combination thereof. Incertain implementations, displayable image data may comprisevector-based image data, raster-based image data, etc. in certainimplementations, various conversion and/or transformation processes maybe implemented to obtain all or part of such displayable image data.

At example block 1104, at least one perceived routability constraint maybe identified based, at least in part, on at least a portion of thedisplayable image data. Here, for example, one or more lines and/orobject transitions may be identified and possibly identified as one ormore perceived routability constraints based, at least in part, on oneor more threshold values and/or related or other like tests. For exampleas mentioned above in certain implementations certain threshold valuesmay take into consideration a minimum pixilated line measurement, aminimum pixilated object transition measurement, a maximum pixilatednoise measurement, and/or the like or some combination thereof. Further,as mentioned, the additional information regarding common and/orexpected patterns, shapes, positions, colors, etc., possibly associatedwith certain design criteria for an indoor environment, may also betaken into consideration in one or more tests.

At example block 1106, a measure of confidence may be set for aperceived routability constraint, e.g., based, at least in part, on thedisplayable image data. Thus, for example, as mentioned a measure ofconfidence may represent an estimated likelihood that a perceivedroutability constraint represents an actual obstacle to navigation.Hence, such measure of confidence may be based, at least in part, oncertain characteristics of the perceived routability constraint withinthe displayable image data. For example as previously mentioned, incertain instances an estimated likelihood may increase for lines and/orobject transitions that may be of certain longer lengths, wider widths,particular shapes or colors, represented in certain layers, possiblytagged with metadata of certain types, adhering to certain expecteddesign criteria, etc. Conversely, as previously mentioned in certaininstances an estimated likelihood may be decrease for lines and/orobject transitions that may be of certain shorter lengths, narrowerwidths, and/or which may not adhere to certain expected design criteria,etc. In certain example implementations, a measure of confidence may beassociated with a perceived routability constraint in the displayableimage data. In certain example implementations, a measure of confidencemay also or alternatively be associated with an applicable edge of aroutability graph.

In accordance with certain implementations, a measure of confidence mayrepresent some numerical value. In other implementations, a measure ofconfidence may represent some non-numerical value that may serve thesame or similar purpose as a numerical value. By way of example, incertain implementations a measure of confidence may be indicated by anumerical-based weight value between 0 and 100, wherein 0 represents a0% estimated likelihood that a perceived routability constraintrepresents an actual obstacle to navigation and a 100 represents a 100%estimated likelihood that the perceived routability constraintrepresents an actual obstacle to navigation. In another example, ameasure of confidence may be indicated by a non-numerical-based weightvalue, such as, e.g., “H”, “M” and “L”, which may represent,respectively, high, medium, and low estimated likelihoods that aperceived routability constraint represents an actual obstacle tonavigation. Of course it should be recognized that these are just a fewexamples and claimed subject matter is not intended to be limited inthis manner.

If process 1100 is performed by a computing platform at the mobiledevice, then at example block 1108, at least the measure of confidencemay be made used to estimate a trajectory of a mobile device within theindoor environment. In certain example implementations, a measure ofconfidence may comprise a weight value associated with all or part ofthe displayable image data portion of an electronic map. In certainexample implementations a measure of confidence may comprise a weightvalue associated with one or more edges of a routability graphcorresponding to an electronic map. In certain implementations,electronic map may comprise all or part of the routability graphassociated there with. Further, as mentioned in certain implementationsand electronic map may comprise all or part of one or more data filesthat may comprise data and/or computer implementable instructions.

If process 1100 is performed by a computing platform external to themobile device then, at example block 1110, at least the measure ofconfidence may be transmitted to the mobile device, e.g., for use by themobile device in estimating its trajectory within the indoorenvironment.

Attention is drawn next to FIG. 12, which is a flow diagram illustratingan example process 1200 that may be implemented in whole or in part in acomputing platform of a mobile device to provide or otherwise supportmobile device positioning based, at least in part, on one or moreperceived routability constraints that may or may not affect movement ofa mobile device within an indoor environment, in accordance with anexample implementation.

At example block 1202, a measure of confidence for at least oneperceived routability constraint may be received by the mobile device.Here for example, a perceived routability constraint may be identifiedbased, at least in part, on displayable image data of an electronic mapfor an indoor environment. In certain implementations, a measure ofconfidence and/or a perceived routability constraint may be receivedfrom one or more other devices, e.g., using all or part of a process1100. In certain other implementations, a mobile device may itself set ameasure of confidence and/or a perceived routability constraint, e.g.,using all or part of a process 1100. As previously mentioned, a measureof confidence may, for example, be associated with displayable imagedata for the perceived routability constraint and/or an applicable edgewithin a routability graph.

As previously mentioned in the various examples presented herein, incertain implementations, as part of block 1202, e.g., at example block1204, a measure of confidence may be received from another device. Incertain example implementations, as part of block 1202, e.g., at exampleblock 1206, a weight value may be received which is associated with aportion of a line or an object transition identified within adisplayable image data. In certain example implementations, as part ofblock 1202, e.g., at example block 1208, a weight value may be receivedwhich is associated with at least one edge identified within aroutability graph corresponding to at least a portion of a displayableimage data.

In certain implementations, a measure of confidence for at least oneperceived routability constraint may be affected. Here, for example atblock 1210, a measure of confidence may be affected based on positioningdata received by the mobile device and/or from one or more other mobiledevices, and/or other computing platforms. Thus for example, a measureof confidence may change based on the feedback provided by suchpositioning data.

In another example, at block 1212, a measure of confidence may beaffected based on an age or some other information associated with theelectronic map and/or the like. Thus for example, a measure ofconfidence may change over time as the underlying information ages.Here, for example, an office layout which uses cubicles or other movableobstacles may change over time and hence an electronic map may be lessreliable as it ages.

At example block 1214, a trajectory of the mobile device within theindoor environment may be estimated, e.g., based, at least in part, onat least one measure of confidence for at least one perceivedroutability constraint. Thus, for example at block 1216, a weight valueassociated with part of the displayable image data and/or applicableedge of a routability graph may be used to affect a particle filteringmodel and/or the like which may be used to estimate a trajectory along afeasible path within an indoor environment taking into account obstaclesto navigation that may be present therein. By way of example, a particlefiltering model and/or the like may take into consideration certainpositioning information that may be received using signal-basedpositioning techniques, inertial sensor positioning techniques,environmental sensor positioning techniques, user input positioningtechniques, and/or the like or some combination thereof.

At example block 1218, estimated positioning information may bepresented to the user (e.g., via one or more output units), wherein theestimated positioning information is based, at least in part, on theestimated trajectory. Thus, for example, in certain implementations oneor more audio and/or visual cues may be provided to a user by one ormore of the mobile device's output units to inform the user in somemanner with regard to positioning and/or navigation. Hence, for examplein certain implementations, an estimated position and/or trajectory orother information may be presented via a visible display and/or audiblesignal along with other details regarding the indoor environment asencoded in an electronic map thereof.

In certain example implementations, an estimated trajectory of a mobiledevice may be computed subject to constraints set forth in a routabilitygraph and according to a motion model such as a particle filtering modeland/or the like. Here, for example, “particles” may represent possiblemotion states (e.g., location, speed, heading, etc.) that may be updatedor computed by incorporating new measurements (e.g., by ranging totransmitters at fixed locations, processing signals generated byinertial and/or environmental sensors, etc.). For example, probabilitiesassigned to particles may be updated or propagated based, at least inpart, on new measurements. These probabilities may be affected by acurrent motion state relative to routing constraints. Erroneous routingconstraints (e.g., from erroneous map features) may adversely affect orskew probabilities applied to particles, which may distort thecomputation of trajectories based, at least in part, on the propagatedparticles. In a particular example, raster-based image data may, attimes, be fuzzy, which may leave certain represented features difficultto identify. The difference and/or transitional demarcation betweenwalls and other features such as stairs, furniture and doors maytherefore be unclear. Also, maps may be out of date (e.g. remodeling hasmoved the walls) or otherwise incorrect.

Current binary approaches tend to assume that either there is anobstacle, across which no movement is allowed, or no obstacle exists, inwhich case movement is free. If a path is incorrectly consideredinfeasible (due to a presumed obstacle), then no movement is allowed,e.g. particles in particle filter are not allowed to cross. This mayresults in trapping of particles, and grossly incorrect positionestimates. With current approaches, to be conservative, one may decideto simply err on the side of NOT imposing an obstacle to navigation, butdoing so tends to significantly dilute the effect of availableinformation.

As presented in some of the example techniques herein, a soft trade-offbetween accuracy of map/assistance data and accuracy of estimates may beachieved in certain instances by using one or more correspondingmeasures of confidence with regard to one or more perceived routabilityconstraints. Such measures of confidence may, for example, be applied asweight values which may be applied to and/or otherwise considered insome manner within propagating or otherwise updating particlesaccordingly.

The methodologies described herein may be implemented by various meansdepending upon applications according to particular features and/orexamples. For example, such methodologies may be implemented inhardware, firmware, and/or combinations thereof, along with software. Ina hardware implementation, for example, a processing unit may beimplemented within one or more application specific integrated circuits(ASICs), digital signal processors (DSPs), digital signal processingdevices (DSPDs), programmable logic devices (PLDs), field programmablegate arrays (FPGAs), processors, controllers, micro-controllers,microprocessors, electronic devices, other devices units designed toperform the functions described herein, and/or combinations thereof.

In the preceding detailed description, numerous specific details havebeen set forth to provide a thorough understanding of claimed subjectmatter. However, it will be understood by those skilled in the art thatclaimed subject matter may be practiced without these specific details.In other instances, methods and apparatuses that would be known by oneof ordinary skill have not been described in detail so as not to obscureclaimed subject matter.

Some portions of the preceding detailed description have been presentedin terms of algorithms or symbolic representations of operations onbinary digital electronic signals stored within a memory of a specificapparatus or special purpose computing device or platform. In thecontext of this particular specification, the term specific apparatus orthe like includes a general purpose computer once it is programmed toperform particular functions pursuant to instructions from programsoftware. Algorithmic descriptions or symbolic representations areexamples of techniques used by those of ordinary skill in the signalprocessing or related arts to convey the substance of their work toothers skilled in the art. An algorithm is here, and generally, isconsidered to be a self-consistent sequence of operations or similarsignal processing leading to a desired result. In this context,operations or processing involve physical manipulation of physicalquantities. Typically, although not necessarily, such quantities maytake the form of electrical or magnetic signals capable of being stored,transferred, combined, compared or otherwise manipulated as electronicsignals representing information. It has proven convenient at times,principally for reasons of common usage, to refer to such signals asbits, data, values, elements, symbols, characters, terms, numbers,numerals, information, or the like. It should be understood, however,that all of these or similar terms are to be associated with appropriatephysical quantities and are merely convenient labels. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout this specificationdiscussions utilizing terms such as processing, computing, calculating,determining, establishing, generating, obtaining, accessing,identifying, setting, applying, associated, and/or the like may refer toactions or processes of a specific apparatus, such as a special purposecomputer or a similar special purpose electronic computing device. Inthe context of this specification, therefore, a special purpose computeror a similar special purpose electronic computing device is capable ofmanipulating or transforming signals, typically represented as physicalelectronic or magnetic quantities within memories, registers, or otherinformation storage devices, transmission devices, or display devices ofthe special purpose computer or similar special purpose electroniccomputing device. In the context of this particular patent application,the term “specific apparatus” may include a general purpose computeronce it is programmed to perform particular functions pursuant toinstructions from program software.

The terms, “and”, “or”, and “and/or” as used herein may include avariety of meanings that also are expected to depend at least in partupon the context in which such terms are used. Typically, “or” if usedto associate a list, such as A, B or C, is intended to mean A, B, and C,here used in the inclusive sense, as well as A, B or C, here used in theexclusive sense. In addition, the term “one or more” as used herein maybe used to describe any feature, structure, or characteristic in thesingular or may be used to describe a plurality or some othercombination of features, structures or characteristics. Though, itshould be noted that this is merely an illustrative example and claimedsubject matter is not limited to this example.

While there has been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein.

Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within the scope of appendedclaims, and equivalents thereof.

What is claimed is:
 1. A method for supporting mobile devicepositioning, the method comprising, with at least one computingplatform: receiving an electronic map for an indoor environment, whereinat least a portion of said electronic map comprises displayable imagedata; identifying at least one perceived routability constraint based,at least in part, on said displayable image data; setting a measure ofconfidence for said at least one perceived routability constraint based,at least in part, on said displayable image data; if said computingplatform is part of a mobile device, estimating a trajectory of saidmobile device within said indoor environment based, at least in part, onsaid measure of confidence; and if said computing platform is part of acomputing device external to said mobile device, transmitting at leastsaid measure of confidence to said mobile device for use by said mobiledevice in estimating said trajectory of said mobile device within saidindoor environment.
 2. The method as recited in claim 1, wherein atleast a portion of said displayable image data comprises vector-basedimage data.
 3. The method as recited in claim 1, wherein at least aportion of said displayable image data comprises raster-based imagedata.
 4. The method as recited in claim 1, wherein at least a portion ofsaid displayable image data represents a plurality of pixelscorresponding to at least a portion of a line or an object transition,and wherein identifying said at least one perceived routabilityconstraint further comprises: identifying at least said plurality ofpixels as said at least one perceived routability constraint based, atleast in part, on one or more threshold values.
 5. The method as recitedin claim 4, wherein at least one of said one or more threshold values isbased, at least in part, on at least one of: a minimum pixilated linemeasurement; a minimum pixilated object transition measurement; or amaximum pixilated noise measurement.
 6. The method as recited in claim1, wherein said at least one perceived routability constraintcorresponds to at least a portion of a line or an object transitionidentified within said displayable image data, and wherein setting saidmeasure of confidence for said at least one perceived routabilityconstraint further comprises: associating a weight value with said atleast said portion of said line or said object transition identifiedwithin said displayable image data, said weight value identifying anestimated likelihood that said at least one perceived routabilityconstraint represents at least part of an actual obstacle within saidindoor environment capable of affecting movement of said mobile devicewithin said indoor environment.
 7. The method as recited in claim 6, andfurther comprising, with said at least one computing platform: affectinga propagation of at least one particle within a particle filtering modelbased, at least in part, on said weight value associated with said atleast said portion of said line or said object transition.
 8. The methodas recited in claim 1, wherein said at least one perceived routabilityconstraint corresponds to at least one edge identified within aroutability graph corresponding to at least a portion of saiddisplayable image data, and wherein setting said measure of confidencefor said at least one perceived routability constraint furthercomprises: associating a weight value with said at least one edge, saidweight value identifying an estimated likelihood that said at least oneperceived routability constraint represents at least part of an actualobstacle within said indoor environment capable of affecting saidtrajectory of said mobile device along said at least one edge within acorresponding region of said indoor environment.
 9. The method asrecited in claim 8, and further comprising, with said at least onecomputing platform: affecting a propagation of at least one particlewithin a particle filtering model based, at least in part, on saidweight value associated with said at least one edge.
 10. The method asrecited in claim 1, and further comprising, with said at least onecomputing platform: affecting said at least said measure of confidencebased, at least in part, on subsequently received positioning data forat least one of said mobile device or one or more other mobile devicesas result of movement therein within said indoor environment.
 11. Themethod as recited in claim 1, and further comprising, with said at leastone computing platform: affecting said at least said measure ofconfidence based, at least in part, on an age of said electronic map.12. The method as recited in claim 1, wherein said at least onecomputing platform is provided within said mobile device, and furthercomprising, with said at least one computing platform: presentingestimated positioning information to a user of said mobile device, saidestimated positioning information being based, at least in part, on saidtrajectory of said mobile device within said indoor environment.
 13. Themethod as recited in claim 12, wherein said estimated positioninginformation is based, at least in part, on said electronic map.
 14. Anapparatus for use in a mobile device, the apparatus comprising: meansfor receiving an electronic map for an indoor environment, wherein atleast a portion of said electronic map comprises displayable image data;means for identifying at least one perceived routability constraintbased, at least in part, on said displayable image data; means forsetting a measure of confidence for said at least one perceivedroutability constraint based, at least in part, on said displayableimage data; means for estimating a trajectory of said mobile devicewithin said indoor environment based, at least in part, on said measureof confidence; and means for presenting estimated positioninginformation to a user of said mobile device, said estimated positioninginformation being based, at least in part, on said trajectory of saidmobile device within said indoor environment.
 15. The apparatus asrecited in claim 14, wherein at least a portion of said displayableimage data represents a plurality of pixels corresponding to at least aportion of a line or an object transition, and further comprising: meansfor identifying at least said plurality of pixels as said at least oneperceived routability constraint based, at least in part, on one or morethreshold values.
 16. The apparatus as recited in claim 15, wherein atleast one of said one or more threshold values is based, at least inpart, on at least one of: a minimum pixilated line measurement; aminimum pixilated object transition measurement; or a maximum pixilatednoise measurement.
 17. The apparatus as recited in claim 14, whereinsaid at least one perceived routability constraint corresponds to atleast a portion of a line or an object transition identified within saiddisplayable image data, and further comprising: means for associating aweight value with said at least said portion of said line or said objecttransition identified within said displayable image data, said weightvalue identifying an estimated likelihood that said at least oneperceived routability constraint represents at least part of an actualobstacle within said indoor environment capable of affecting movement ofsaid mobile device within said indoor environment.
 18. The apparatus asrecited in claim 17, and further comprising: means for affecting apropagation of at least one particle within a particle filtering modelbased, at least in part, on said weight value associated with said atleast said portion of said line or said object transition.
 19. Theapparatus as recited in claim 14, wherein said at least one perceivedroutability constraint corresponds to at least one edge identifiedwithin a routability graph corresponding to at least a portion of saiddisplayable image data, and further comprising: means for associating aweight value with said at least one edge, said weight value identifyingan estimated likelihood that said at least one perceived routabilityconstraint represents at least part of an actual obstacle within saidindoor environment capable of affecting said trajectory of said mobiledevice along said at least one edge within a corresponding region ofsaid indoor environment.
 20. The apparatus as recited in claim 19, andfurther comprising: means for affecting a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least one edge.
 21. Theapparatus as recited in claim 14, and further comprising: means foraffecting said at least said measure of confidence based, at least inpart, on subsequently received positioning data for at least one of saidmobile device or one or more other mobile devices as result of movementtherein within said indoor environment.
 22. The apparatus as recited inclaim 14, and further comprising: means for affecting said at least saidmeasure of confidence based, at least in part, on an age of saidelectronic map.
 23. An apparatus for use in a computing device externalto a mobile device to support mobile device positioning, the apparatuscomprising: means for receiving an electronic map for an indoorenvironment, wherein at least a portion of said electronic map comprisesdisplayable image data; means for identifying at least one perceivedroutability constraint based, at least in part, on said displayableimage data; means for setting a measure of confidence for said at leastone perceived routability constraint based, at least in part, on saiddisplayable image data; and means for transmitting at least said measureof confidence to said mobile device for use by said mobile device inestimating a trajectory of said mobile device within said indoorenvironment.
 24. The apparatus as recited in claim 23, wherein at leasta portion of said displayable image data represents a plurality ofpixels corresponding to at least a portion of a line or an objecttransition, and further comprising: means for identifying at least saidplurality of pixels as said at least one perceived routabilityconstraint based, at least in part, on one or more threshold values. 25.The apparatus as recited in claim 24, wherein at least one of said oneor more threshold values is based, at least in part, on at least one of:a minimum pixilated line measurement; a minimum pixilated objecttransition measurement; or a maximum pixilated noise measurement. 26.The apparatus as recited in claim 23, wherein said at least oneperceived routability constraint corresponds to at least a portion of aline or an object transition identified within said displayable imagedata, and further comprising: means for associating a weight value withsaid at least said portion of said line or said object transitionidentified within said displayable image data, said weight valueidentifying an estimated likelihood that said at least one perceivedroutability constraint represents at least part of an actual obstaclewithin said indoor environment capable of affecting movement of saidmobile device within said indoor environment.
 27. The apparatus asrecited in claim 26, and further comprising: means for affecting apropagation of at least one particle within a particle filtering modelbased, at least in part, on said weight value associated with said atleast said portion of said line or said object transition.
 28. Theapparatus as recited in claim 23, wherein said at least one perceivedroutability constraint corresponds to at least one edge identifiedwithin a routability graph corresponding to at least a portion of saiddisplayable image data, and further comprising: means for associating aweight value with said at least one edge, said weight value identifyingan estimated likelihood that said at least one perceived routabilityconstraint represents at least part of an actual obstacle within saidindoor environment capable of affecting a trajectory of said mobiledevice along said at least one edge within a corresponding region ofsaid indoor environment.
 29. The apparatus as recited in claim 28, andfurther comprising: means for affecting a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least one edge.
 30. Theapparatus as recited in claim 23, and further comprising: means foraffecting said at least said measure of confidence based, at least inpart, on subsequently received positioning data for at least one of saidmobile device or one or more other mobile devices as result of movementtherein within said indoor environment.
 31. The apparatus as recited inclaim 23, and further comprising: means for affecting said at least saidmeasure of confidence based, at least in part, on an age of saidelectronic map.
 32. A mobile device comprising: memory; an output unit;and one or more processing units to: access an electronic map for anindoor environment via said memory, wherein at least a portion of saidelectronic map comprises displayable image data; identify at least oneperceived routability constraint based, at least in part, on saiddisplayable image data; set a measure of confidence for said at leastone perceived routability constraint based, at least in part, on saiddisplayable image data; estimate a trajectory of said mobile devicewithin said indoor environment based, at least in part, on said measureof confidence; and initiate presentation of estimated positioninginformation to a user of said mobile device via said output unit, saidestimated positioning information being based, at least in part, on saidtrajectory of said mobile device within said indoor environment.
 33. Themobile device as recited in claim 32, wherein at least a portion of saiddisplayable image data represents a plurality of pixels corresponding toat least a portion of a line or an object transition, and said one ormore processing units to further: identify at least said plurality ofpixels as said at least one perceived routability constraint based, atleast in part, on one or more threshold values.
 34. The mobile device asrecited in claim 33, wherein at least one of said one or more thresholdvalues is based, at least in part, on at least one of: a minimumpixilated line measurement; a minimum pixilated object transitionmeasurement; or a maximum pixilated noise measurement.
 35. The mobiledevice as recited in claim 32, wherein said at least one perceivedroutability constraint corresponds to at least a portion of a line or anobject transition identified within said displayable image data, andsaid one or more processing units to further: associate a weight valuewith said at least said portion of said line or said object transitionidentified within said displayable image data, said weight valueidentifying an estimated likelihood that said at least one perceivedroutability constraint represents at least part of an actual obstaclewithin said indoor environment capable of affecting movement of saidmobile device within said indoor environment.
 36. The mobile device asrecited in claim 35, said one or more processing units to further:affect a propagation of at least one particle within a particlefiltering model based, at least in part, on said weight value associatedwith said at least said portion of said line or said object transition.37. The mobile device as recited in claim 32, wherein said at least oneperceived routability constraint corresponds to at least one edgeidentified within a routability graph corresponding to at least aportion of said displayable image data, and said one or more processingunits to further: associate a weight value with said at least one edge,said weight value identifying an estimated likelihood that said at leastone perceived routability constraint represents at least part of anactual obstacle within said indoor environment capable of affecting atrajectory of said mobile device along said at least one edge within acorresponding region of said indoor environment.
 38. The mobile deviceas recited in claim 37, said one or more processing units to further:affect a propagation of at least one particle within a particlefiltering model based, at least in part, on said weight value associatedwith said at least one edge.
 39. The mobile device as recited in claim32, said one or more processing units to further: affect said at leastsaid measure of confidence based, at least in part, on subsequentlyreceived positioning data for at least one of said mobile device or oneor more other mobile devices as result of movement therein within saidindoor environment.
 40. The mobile device as recited in claim 32, saidone or more processing units to further: affect said at least saidmeasure of confidence based, at least in part, on an age of saidelectronic map.
 41. A computing device for use in supporting mobiledevice positioning, the computing device comprising: memory; acommunication interface; and one or more processing units to: access anelectronic map for an indoor environment via said memory, wherein atleast a portion of said electronic map comprises displayable image data;identify at least one perceived routability constraint based, at leastin part, on said displayable image data; set a measure of confidence forsaid at least one perceived routability constraint based, at least inpart, on said displayable image data; and initiate transmission, viasaid communication interface, of at least said measure of confidence tosaid mobile device for use in estimating a trajectory of said mobiledevice within said indoor environment.
 42. The computing device asrecited in claim 41, wherein at least a portion of said displayableimage data represents a plurality of pixels corresponding to at least aportion of a line or an object transition, and said one or moreprocessing units to further: identify at least said plurality of pixelsas said at least one perceived routability constraint based, at least inpart, on one or more threshold values.
 43. The computing device asrecited in claim 42, wherein at least one of said one or more thresholdvalues is based, at least in part, on at least one of: a minimumpixilated line measurement; a minimum pixilated object transitionmeasurement; or a maximum pixilated noise measurement.
 44. The computingdevice as recited in claim 41, wherein said at least one perceivedroutability constraint corresponds to at least a portion of a line or anobject transition identified within said displayable image data, andsaid one or more processing units to further: associate a weight valuewith said at least said portion of said line or said object transitionidentified within said displayable image data, said weight valueidentifying an estimated likelihood that said at least one perceivedroutability constraint represents at least part of an actual obstaclewithin said indoor environment capable of affecting movement of saidmobile device within said indoor environment.
 45. The computing deviceas recited in claim 44, said one or more processing units to further:affect a propagation of at least one particle within a particlefiltering model based, at least in part, on said weight value associatedwith said at least said portion of said line or said object transition.46. The computing device as recited in claim 41, wherein said at leastone perceived routability constraint corresponds to at least one edgeidentified within a routability graph corresponding to at least aportion of said displayable image data, and said one or more processingunits to further: associate a weight value with said at least one edge,said weight value identifying an estimated likelihood that said at leastone perceived routability constraint represents at least part of anactual obstacle within said indoor environment capable of affecting atrajectory of said mobile device along said at least one edge within acorresponding region of said indoor environment.
 47. The computingdevice as recited in claim 46, said one or more processing units tofurther: affect a propagation of at least one particle within a particlefiltering model based, at least in part, on said weight value associatedwith said at least one edge.
 48. The computing device as recited inclaim 41, said one or more processing units to further: affect said atleast said measure of confidence based, at least in part, onsubsequently received positioning data for at least one of said mobiledevice or one or more other mobile devices as result of movement thereinwithin said indoor environment.
 49. The computing device as recited inclaim 41, said one or more processing units to further: affect said atleast said measure of confidence based, at least in part, on an age ofsaid electronic map.
 50. An article for use in a computing device tosupport mobile device positioning, the article comprising: anon-transitory computer readable medium having computer implementableinstructions stored therein that are executable by one or moreprocessing units of a computing platform of a mobile device to: receivean electronic map for an indoor environment, wherein at least a portionof said electronic map comprises displayable image data; identify atleast one perceived routability constraint based, at least in part, onsaid displayable image data; set a measure of confidence for said atleast one perceived routability constraint based, at least in part, onsaid displayable image data; estimate a trajectory of said mobile devicewithin said indoor environment based, at least in part, on said measureof confidence; and initiate presentation of estimated positioninginformation to a user of said mobile device, said estimated positioninginformation being based, at least in part, on said trajectory of saidmobile device within said indoor environment.
 51. The article as recitedin claim 50, wherein at least a portion of said displayable image datarepresents a plurality of pixels corresponding to at least a portion ofa line or an object transition, and said computer implementableinstructions are further executable by said one or more processing unitsto: identify at least said plurality of pixels as said at least oneperceived routability constraint based, at least in part, on one or morethreshold values.
 52. The article as recited in claim 51, wherein atleast one of said one or more threshold values is based, at least inpart, on at least one of: a minimum pixilated line measurement; aminimum pixilated object transition measurement; or a maximum pixilatednoise measurement.
 53. The article as recited in claim 50, wherein saidat least one perceived routability constraint corresponds to at least aportion of a line or an object transition identified within saiddisplayable image data, and said computer implementable instructions arefurther executable by said one or more processing units to: associate aweight value with said at least said portion of said line or said objecttransition identified within said displayable image data, said weightvalue identifying an estimated likelihood that said at least oneperceived routability constraint represents at least part of an actualobstacle within said indoor environment capable of affecting movement ofsaid mobile device within said indoor environment.
 54. The article asrecited in claim 53, said computer implementable instructions arefurther executable by said one or more processing units to: affect apropagation of at least one particle within a particle filtering modelbased, at least in part, on said weight value associated with said atleast said portion of said line or said object transition.
 55. Thearticle as recited in claim 50, wherein said at least one perceivedroutability constraint corresponds to at least one edge identifiedwithin a routability graph corresponding to at least a portion of saiddisplayable image data, and said computer implementable instructions arefurther executable by said one or more processing units to: associate aweight value with said at least one edge, said weight value identifyingan estimated likelihood that said at least one perceived routabilityconstraint represents at least part of an actual obstacle within saidindoor environment capable of affecting a trajectory of said mobiledevice along said at least one edge within a corresponding region ofsaid indoor environment.
 56. The article as recited in claim 55, saidcomputer implementable instructions are further executable by said oneor more processing units to: affect a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least one edge.
 57. Thearticle as recited in claim 50, and said computer implementableinstructions are further executable by said one or more processing unitsto: affect said at least said measure of confidence based, at least inpart, on subsequently received positioning data for at least one of saidmobile device or one or more other mobile devices as result of movementtherein within said indoor environment.
 58. The article as recited inclaim 50, said computer implementable instructions are furtherexecutable by said one or more processing units to: affect said at leastsaid measure of confidence based, at least in part, on an age of saidelectronic map.
 59. An article for use in a computing platform externalto a mobile device to support mobile device positioning, the articlecomprising: a non-transitory computer readable medium having computerimplementable instructions stored therein that are executable by one ormore processing units of the computing platform to: receive anelectronic map for an indoor environment, wherein at least a portion ofsaid electronic map comprises displayable image data; identify at leastone perceived routability constraint based, at least in part, on saiddisplayable image data; set a measure of confidence for said at leastone perceived routability constraint based, at least in part, on saiddisplayable image data; and initiate transmission of at least saidmeasure of confidence to said mobile device for use in estimating atrajectory of said mobile device within said indoor environment.
 60. Thearticle as recited in claim 59, wherein at least a portion of saiddisplayable image data represents a plurality of pixels corresponding toat least a portion of a line or an object transition, and said computerimplementable instructions are further executable by said one or moreprocessing units to: identify at least said plurality of pixels as saidat least one perceived routability constraint based, at least in part,on one or more threshold values.
 61. The article as recited in claim 60,wherein at least one of said one or more threshold values is based, atleast in part, on at least one of: a minimum pixilated line measurement;a minimum pixilated object transition measurement; or a maximumpixilated noise measurement.
 62. The article as recited in claim 59,wherein said at least one perceived routability constraint correspondsto at least a portion of a line or an object transition identifiedwithin said displayable image data, and said computer implementableinstructions are further executable by said one or more processing unitsto: associate a weight value with said at least said portion of saidline or said object transition identified within said displayable imagedata, said weight value identifying an estimated likelihood that said atleast one perceived routability constraint represents at least part ofan actual obstacle within said indoor environment capable of affectingmovement of said mobile device within said indoor environment.
 63. Thearticle as recited in claim 62, said computer implementable instructionsare further executable by said one or more processing units to: affect apropagation of at least one particle within a particle filtering modelbased, at least in part, on said weight value associated with said atleast said portion of said line or said object transition.
 64. Thearticle as recited in claim 59, wherein said at least one perceivedroutability constraint corresponds to at least one edge identifiedwithin a routability graph corresponding to at least a portion of saiddisplayable image data, and said computer implementable instructions arefurther executable by said one or more processing units to: associate aweight value with said at least one edge, said weight value identifyingan estimated likelihood that said at least one perceived routabilityconstraint represents at least part of an actual obstacle within saidindoor environment capable of affecting a trajectory of said mobiledevice along said at least one edge within a corresponding region ofsaid indoor environment.
 65. The article as recited in claim 64, saidcomputer implementable instructions are further executable by said oneor more processing units to: affect a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least one edge.
 66. Thearticle as recited in claim 59, and said computer implementableinstructions are further executable by said one or more processing unitsto: affect said at least said measure of confidence based, at least inpart, on subsequently received positioning data for at least one of saidmobile device or one or more other mobile devices as result of movementtherein within said indoor environment.
 67. The article as recited inclaim 59, said computer implementable instructions are furtherexecutable by said one or more processing units to: affect said at leastsaid measure of confidence based, at least in part, on an age of saidelectronic map.
 68. A method for mobile device positioning, the methodcomprising, with a mobile device: receiving a measure of confidence forat least one perceived routability constraint identified based, at leastin part, on displayable image data of an electronic map for an indoorenvironment; estimating a trajectory of said mobile device within saidindoor environment based, at least in part, on said measure ofconfidence for said at least one perceived routability constraint; andpresenting estimated positioning information to a user of said mobiledevice, said estimated positioning information being based, at least inpart, on said trajectory of said mobile device within said indoorenvironment.
 69. The method as recited in claim 68, wherein said measureof confidence for said at least one perceived routability constraintcomprises a weight value associated with at least a portion of a line oran object transition identified within said displayable image data, saidweight value identifying an estimated likelihood that said at least oneperceived routability constraint represents at least part of an actualobstacle within said indoor environment capable of affecting movement ofsaid mobile device within said indoor environment.
 70. The method asrecited in claim 69, and further comprising: affecting a propagation ofat least one particle within a particle filtering model based, at leastin part, on said weight value associated with said at least said portionof said line or said object transition.
 71. The method as recited inclaim 68, wherein said measure of confidence for said at least oneperceived routability constraint comprises a weight value associatedwith at least one edge identified within a routability graphcorresponding to at least a portion of said displayable image data, saidweight value identifying an estimated likelihood that said at least oneperceived routability constraint represents at least part of an actualobstacle within said indoor environment capable of affecting atrajectory of said mobile device along said at least one edge within acorresponding region of said indoor environment.
 72. The method asrecited in claim 71, and further comprising: affecting a propagation ofat least one particle within a particle filtering model based, at leastin part, on said weight value associated with said at least one edge.73. The method as recited in claim 68, wherein receiving said measure ofconfidence for said at least one perceived routability constraintfurther comprises: receiving said at least said measure of confidencefrom another device.
 74. The method as recited in claim 68, and furthercomprising: affecting said at least said measure of confidence based, atleast in part, on subsequently received positioning data for at leastone of said mobile device or one or more other mobile devices as resultof movement therein within said indoor environment.
 75. The method asrecited in claim 68, and further comprising: affecting said at leastsaid measure of confidence based, at least in part, on an age of saidelectronic map.
 76. An apparatus to provide mobile device positioning,the apparatus comprising: means for receiving a measure of confidencefor at least one perceived routability constraint identified based, atleast in part, on displayable image data of an electronic map for anindoor environment; means for estimating a trajectory of said mobiledevice within said indoor environment based, at least in part, on saidmeasure of confidence for said at least one perceived routabilityconstraint; and means for presenting estimated positioning informationto a user of said mobile device, said estimated positioning informationbeing based, at least in part, on said trajectory of said mobile devicewithin said indoor environment.
 77. The apparatus as recited in claim76, wherein said measure of confidence for said at least one perceivedroutability constraint comprises a weight value associated with at leasta portion of a line or an object transition identified within saiddisplayable image data, said weight value identifying an estimatedlikelihood that said at least one perceived routability constraintrepresents at least part of an actual obstacle within said indoorenvironment capable of affecting movement of said mobile device withinsaid indoor environment.
 78. The apparatus as recited in claim 77, andfurther comprising: means for affecting a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least said portion of saidline or said object transition.
 79. The apparatus as recited in claim76, wherein said measure of confidence for said at least one perceivedroutability constraint comprises a weight value associated with at leastone edge identified within a routability graph corresponding to at leasta portion of said displayable image data, said weight value identifyingan estimated likelihood that said at least one perceived routabilityconstraint represents at least part of an actual obstacle within saidindoor environment capable of affecting a trajectory of said mobiledevice along said at least one edge within a corresponding region ofsaid indoor environment.
 80. The apparatus as recited in claim 79, andfurther comprising: means for affecting a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least one edge.
 81. Theapparatus as recited in claim 76, and further comprising: means forreceiving said at least said measure of confidence from another device.82. The apparatus as recited in claim 76, and further comprising: meansfor affecting said at least said measure of confidence based, at leastin part, on subsequently received positioning data for at least one ofsaid mobile device or one or more other mobile devices as result ofmovement therein within said indoor environment.
 83. The apparatus asrecited in claim 76, and further comprising: means for affecting said atleast said measure of confidence based, at least in part, on an age ofsaid electronic map.
 84. A mobile device having a positioningcapability, the mobile device comprising: memory; an output unit; andone or more processing units to: receive, via said memory, a measure ofconfidence for at least one perceived routability constraint identifiedbased, at least in part, on displayable image data of an electronic mapfor an indoor environment; estimate a trajectory of said mobile devicewithin said indoor environment based, at least in part, on said measureof confidence for said at least one perceived routability constraint;and initiate presentation of estimated positioning information to a userof said mobile device via said output unit, said estimated positioninginformation being based, at least in part, on said trajectory of saidmobile device within said indoor environment.
 85. The mobile device asrecited in claim 84, wherein said measure of confidence for said atleast one perceived routability constraint comprises a weight valueassociated with at least a portion of a line or an object transitionidentified within said displayable image data, said weight valueidentifying an estimated likelihood that said at least one perceivedroutability constraint represents at least part of an actual obstaclewithin said indoor environment capable of affecting movement of saidmobile device within said indoor environment.
 86. The mobile device asrecited in claim 85, said one or more processing units to further:affect a propagation of at least one particle within a particlefiltering model based, at least in part, on said weight value associatedwith said at least said portion of said line or said object transition.87. The mobile device as recited in claim 84, wherein said measure ofconfidence for said at least one perceived routability constraintcomprises a weight value associated with at least one edge identifiedwithin a routability graph corresponding to at least a portion of saiddisplayable image data, said weight value identifying an estimatedlikelihood that said at least one perceived routability constraintrepresents at least part of an actual obstacle within said indoorenvironment capable of affecting a trajectory of said mobile devicealong said at least one edge within a corresponding region of saidindoor environment.
 88. The mobile device as recited in claim 87, saidone or more processing units to further: affect a propagation of atleast one particle within a particle filtering model based, at least inpart, on said weight value associated with said at least one edge. 89.The mobile device as recited in claim 84, and further comprising: acommunication interface; and said one or more processing units tofurther: receive said at least said measure of confidence from anotherdevice via said communication interface.
 90. The mobile device asrecited in claim 84, said one or more processing units to further:affect said at least said measure of confidence based, at least in part,on subsequently received positioning data for at least one of saidmobile device or one or more other mobile devices as result of movementtherein within said indoor environment.
 91. The mobile device as recitedin claim 84, said one or more processing units to further: affect saidat least said measure of confidence based, at least in part, on an ageof said electronic map.
 92. An article for use in a mobile device toprovide for mobile device positioning, the article comprising: anon-transitory computer readable medium having computer implementableinstructions stored therein that are executable by one or moreprocessing units of a computing platform to: receive a measure ofconfidence for at least one perceived routability constraint identifiedbased, at least in part, on displayable image data of an electronic mapfor an indoor environment; estimate a trajectory of said mobile devicewithin said indoor environment based, at least in part, on said measureof confidence for said at least one perceived routability constraint;and initiate presentation of estimated positioning information to a userof said mobile device, said estimated positioning information beingbased, at least in part, on said trajectory of said mobile device withinsaid indoor environment.
 93. The article as recited in claim 92, whereinsaid measure of confidence for said at least one perceived routabilityconstraint comprises a weight value associated with at least a portionof a line or an object transition identified within said displayableimage data, said weight value identifying an estimated likelihood thatsaid at least one perceived routability constraint represents at leastpart of an actual obstacle within said indoor environment capable ofaffecting movement of said mobile device within said indoor environment.94. The article as recited in claim 93, said computer implementableinstructions are further executable by said one or more processing unitsto: affect a propagation of at least one particle within a particlefiltering model based, at least in part, on said weight value associatedwith said at least said portion of said line or said object transition.95. The article as recited in claim 92, wherein said measure ofconfidence for said at least one perceived routability constraintcomprises a weight value associated with at least one edge identifiedwithin a routability graph corresponding to at least a portion of saiddisplayable image data, said weight value identifying an estimatedlikelihood that said at least one perceived routability constraintrepresents at least part of an actual obstacle within said indoorenvironment capable of affecting a trajectory of said mobile devicealong said at least one edge within a corresponding region of saidindoor environment.
 96. The article as recited in claim 95, saidcomputer implementable instructions are further executable by said oneor more processing units to: affect a propagation of at least oneparticle within a particle filtering model based, at least in part, onsaid weight value associated with said at least one edge.
 97. Thearticle as recited in claim 92, said computer implementable instructionsare further executable by said one or more processing units to: receivesaid at least said measure of confidence from another device.
 98. Thearticle as recited in claim 92, said computer implementable instructionsare further executable by said one or more processing units to: affectsaid at least said measure of confidence based, at least in part, onsubsequently received positioning data for at least one of said mobiledevice or one or more other mobile devices as result of movement thereinwithin said indoor environment.
 99. The article as recited in claim 92,said computer implementable instructions are further executable by saidone or more processing units to: affect said at least said measure ofconfidence based, at least in part, on an age of said electronic map.